M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2025
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2024
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
prof. RNDr. Ivanka Horová, CSc. (alternate examiner)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 19. 2. to Sun 26. 5. Wed 10:00–11:50 M6,01011
  • Timetable of Seminar Groups:
M8113/01: Mon 19. 2. to Sun 26. 5. Wed 12:00–12:50 MP1,01014, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2023
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 10:00–11:50 M4,01024
  • Timetable of Seminar Groups:
M8113/01: Wed 12:00–12:50 MP2,01014a, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2022
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M8113/01: Tue 12:00–12:50 MP2,01014a, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2021
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 1. 3. to Fri 14. 5. Tue 8:00–9:50 online_M3
  • Timetable of Seminar Groups:
M8113/01: Mon 1. 3. to Fri 14. 5. Thu 12:00–12:50 online_MP1, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2020
Extent and Intensity
2/1/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. Jan Koláček, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M8113/01: Thu 12:00–12:50 MP1,01014, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates.
Learning outcomes
Student will be able:
- to analyze a given set of real dat;
- to propose a suitable method for data processing;
- to give implementation and create computer programs;
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All presented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2019
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 18. 2. to Fri 17. 5. Thu 8:00–9:50 M3,01023
  • Timetable of Seminar Groups:
M8113/01: Mon 18. 2. to Fri 17. 5. Wed 12:00–12:50 MP1,01014, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All prsented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
spring 2018
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 12:00–13:50 M3,01023
  • Timetable of Seminar Groups:
M8113/01: Wed 14:00–14:50 MP1,01014, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All prsented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2017
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 20. 2. to Mon 22. 5. Wed 8:00–9:50 M2,01021
  • Timetable of Seminar Groups:
M8113/01: Mon 20. 2. to Mon 22. 5. Fri 12:00–12:50 MP1,01014
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All prsented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
  • HOROVÁ, Ivanka, Jan KOLÁČEK and Jiří ZELINKA. Kernel Smoothing in MATLAB: Theory and Practice of Kernel Smoothing. Singapore: World Scientific Publishing Co. Pte. Ltd., 2012, 244 pp. ISBN 978-981-4405-48-5. URL info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2016
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 12:00–13:50 M5,01013
  • Timetable of Seminar Groups:
M8113/01: Thu 15:00–15:50 MP1,01014, J. Koláček
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density a distribution function, a regression function and bivariate density estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples. All prsented method are implemented in Matlab.The toolbox is available on http://www.math.muni.cz/veda-a-vyzkum/vyvijeny-software/274-matlab-toolbox.html
Literature
    recommended literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including the use of the toolbox
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2015
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 MS1,01016
  • Timetable of Seminar Groups:
M8113/01: Mon 14:00–14:50 MP2,01014a
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture. Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Theory and Practice of Kernel Smoothing

Faculty of Science
Spring 2014
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 M3,01023
  • Timetable of Seminar Groups:
M8113/01: Thu 12:00–12:50 MP2,01014a
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years. The existence of high speed inexpensive computing has made it easy to look at the data in ways that were once impossible. The power of computer now allows great freedom in deciding where an analysis of data should go. One area that has benefited greatly from this new freedom is that of nonparametric density, distribution, and regression function estimation,or what are generally called smoothing methods. This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates. At the end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density, criterion for quality of estimates, problem of a choice of a bandwidth, canonical kernels and optimal kernel theory, kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions, comparision of these estimates, boundary effects problem, criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Jedná se o inovovaný předmět Neparametrické vyhlazování.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2013
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 10:00–11:50 M3,01023
  • Timetable of Seminar Groups:
M8113/01: Wed 10:00–10:50 MP2,01014a
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2012
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M8113/01: Thu 12:00–12:50 MP1,01014
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2011
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 MS1,01016
  • Timetable of Seminar Groups:
M8113/01: Wed 10:00–10:50 MP1,01014
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2010
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 MS1,01016
  • Timetable of Seminar Groups:
M8113/01: Wed 10:00–10:50 MP1,01014, Wed 10:00–10:50 MS1,01016
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2009
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 9:00–10:50 01031
  • Timetable of Seminar Groups:
M8113/01: Thu 12:00–12:50 MP1,01014, Thu 12:00–12:50 MS1,01016
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods
Lecture and a class excercise in a computer room, compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2008
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 N41
  • Timetable of Seminar Groups:
M8113/01: Wed 7:00–7:50 M3,04005 - dříve Janáčkovo nám. 2a, Wed 7:00–7:50 N41
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2007
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Timetable
Tue 11:00–12:50 N41
  • Timetable of Seminar Groups:
M8113/01: Thu 8:00–8:50 M3,04005 - dříve Janáčkovo nám. 2a, Thu 8:00–8:50 N41
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2006
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Timetable
Mon 12:00–13:50 U1
  • Timetable of Seminar Groups:
M8113/01: Wed 7:00–7:50 M3,04005 - dříve Janáčkovo nám. 2a, Wed 7:00–7:50 N41
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2005
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Timetable
Tue 8:00–9:50 N41
  • Timetable of Seminar Groups:
M8113/01: Thu 13:00–13:50 M3,04005 - dříve Janáčkovo nám. 2a, Thu 13:00–13:50 N21, J. Zelinka
M8113/02: No timetable has been entered into IS. J. Zelinka
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2004
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Timetable of Seminar Groups
M8113/01: No timetable has been entered into IS. J. Zelinka
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2003
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Timetable of Seminar Groups
M8113/01: No timetable has been entered into IS. J. Zelinka
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
spring 2012 - acreditation

The information about the term spring 2012 - acreditation is not made public

Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
doc. Mgr. Jan Koláček, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2011 - only for the accreditation
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods of a density and a regression function estimates.Ar rhe end of this course a student should be able to apply these methods in statistical real data processing.
Syllabus
  • Basic idea of smoothing.
  • General principle of kernel estimates.
  • Kernel estimates of a density ,criterion for quality of estimates,problem of a choice of a bandwidth,canonical kernels and optimal kernel theory,kernels of higher orders.
  • Kernel estimates of a distribution function, a choice of a bandwidth.
  • Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates.
  • The presented theory is followed by practical examples .
Literature
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • Smoothing and regression : approaches, computation, and application. Edited by Michael G. Schimek. New York: John Wiley & Sons, 2000, xix, 607. ISBN 0471179469. info
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Teaching methods
Lecture: 2 hours weekly Class excercise: 1 hour weekly. The excercise is aimed at application of methods delivered in the lecture including their presentations in a computer room.
Assessment methods
Lecture.Compulsory attendance of excercises. Oral exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.

M8113 Nonparametric Smoothing

Faculty of Science
Spring 2008 - for the purpose of the accreditation
Extent and Intensity
2/1. 3 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ivanka Horová, CSc. (lecturer)
Mgr. Jiří Zelinka, Dr. (seminar tutor)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: prof. RNDr. Ivanka Horová, CSc.
Prerequisites
Basic knowledge of probability and mathematical statistics
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The theory and methods of smoothing have been developed mainly in the last years.The existence of high speed,inexpensive computing has made it easy to look at the data in ways that were once impossible.The power of computer now allows great freedom in deciding where an analysis of data should go.One area that has benefited greatly from this new freedom is that of nonparametric density,distribution,and regression function estimation,or what are generally called smoothing methods.This subject aims to give a survey of modern nonparametric methods as kernel estimates of univariate and multivariate densities, and kernel estimates of regression functions as well.The smoothing splines are also dealt with.
Syllabus
  • Basic idea of smoothing. General principle of kernel estimates. Kernel estimates of univariate and multivariate densities,criterion for quality of estimates,problem of a choice of a bandwidth,,canonical kernels and optimal kernel theory,kernels of higher orders. Various types of kernel estimates of regression functions,comparision of these estimates,boundary effects problem,criterion for a quality of estimates. Smoothing splines,shape preserving splines. The theory presented at the lecture is followed by practical examples .
Literature
  • SIMONOFF, Jeffrey S. Smoothing methods in statistics. New York: Springer-Verlag, 1996, xii, 338. ISBN 0387947167. info
  • SILVERMAN, B. W. Density estimation for statistics and data analysis. 1st ed. Boca Raton: Chapman & Hall, 1986, ix, 175. ISBN 0412246201. info
  • WAND, M. P. and M. C. JONES. Kernel smoothing. 1st ed. London: Chapman & Hall, 1995, 212 s. ISBN 0412552701. info
  • Statistical theory and computational aspects of smoothing :proceedings of the COMPSTAT '94 satellite meeting held in Semmering, Austria 27-28 August 1994. Edited by Wolfgang Härdle - Michael G. Schimek. Heidelberg: Physica-Verlag, 1996, viii, 265. ISBN 3-7908-0930-6. info
Assessment methods (in Czech)
Přednáška, cvičení v počiačové učebně. Zkouška :ústní
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.