M9901 Theory and practice of spline smoothing

Faculty of Science
autumn 2017
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
Mgr. Vojtěch Šindlář (seminar tutor)
Mgr. Zdeňka Geršlová (assistant)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics - Departments - Faculty of Science
Contact Person: doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
Timetable
Mon 18. 9. to Fri 15. 12. Wed 10:00–11:50 M3,01023
  • Timetable of Seminar Groups:
M9901/01: Mon 18. 9. to Fri 15. 12. Wed 12:00–13:50 MP1,01014, V. Šindlář
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 main goal of the course is to become familiar with interpolation and smoothing using one- and multivariate splines, outlier detection with applications to electrocardiology, electroencephalography, shape analysis (geometrical morphometrics) on biological objects, statistical analyses of multivariate data, testing of multivariate statistical hypotheses, multivariate SVD models (e.g. generalized PCA), 2D/3D statistical visualisation and implementation to R language.
Learning outcomes
Student will be able:
- to understand principles of spline interpolation and smoothing for curves and surfaces;
- to build up and explain suitable model for curves and surfaces;
- to apply spline interpolation and smoothing to real data;
- to implement methods of spline interpolation and smoothing to R.
Syllabus
  • geometric transformations in 2D and 3D,
  • multivariate splines, functional models,
  • identification of anatomical landmarks, curves, and surfaces,
  • testing of multivariate statistical hypotheses,
  • multivariate statistical methods for EEG, ECG, and morphometric data,
  • 2D/3D statistical graphics
Literature
    recommended literature
  • JOHNSON, Richard A. and Dean W. WICHERN. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs: Prentice-Hall, 1992. xiv, 642 s. ISBN 0-13-041807-2. info
    not specified
  • CASELLA, George and Roger L. BERGER. Statistical inference. 2nd ed. Pacific Grove, Calif.: Duxbury, 2002. xxviii, 66. ISBN 0534243126. info
Teaching methods
lectures, practicals, homework
Assessment methods
homework, oral exam
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms Autumn 2011, Spring 2014, Spring 2015, Autumn 2015, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
  • Enrolment Statistics (autumn 2017, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2017/M9901