PřF:M7777 Applied FDA - Course Information
M7777 Applied functional data analysisFaculty of Science
- Extent and Intensity
- 0/2/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
Taught in person.
- doc. Mgr. Jan Koláček, Ph.D. (lecturer)
- 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 of Seminar Groups
- M7777/01: Wed 16:00–17:50 MP2,01014a, J. Koláček
- Elementary knowledge of probability and statistics. Elementary knowledge of work in R.
- 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 8 fields of study the course is directly associated with, display
- Course objectives
- Introduction to the analysis of data that may be considered to be smooth functions. This class will focus on the application of functional data analysis techniques to real-world problems and is not intended to be mathematically technical. Main topics: visualization and data exploration, nonparametric smoothing, functional linear models, functional principal components analysis, analysis involving derivatives, registration.
- Learning outcomes
- On the completion of this course, the student is expected to obtain sufficient mastery of the application of functional data analysis techniques to real-world problems; to model functional data in practical examples; be able to interpret obtained results.
- Introduction to functional data analysis (FDA); various types of analysed data; ''fda´´ package in R. Transformation to the functional form; several types of basis systems; smoothing techniques, cross-validation; constrained smoothing. Exploratory functional data analysis; functional characteristics; functional principal component analysis. Introduction to discriminant analysis; analysis of sparse (longitudinal) data. Functional linear models; several types of response; nonparametric functional regression. Registration.
- recommended literature
- KOKOSZKA, Piotr and Matthew REIMHERR. Introduction to functional data analysis. Boca Raton, FL: CRC Press, Taylor & Francis Group, 2017. xvi, 290. ISBN 9781498746342. info
- HORVÁTH, Lajos and Piotr KOKOSZKA. Inference for functional data with applications. London: Springer, 2012. xiv, 422. ISBN 9781461436546. info
- RAMSAY, James O., Giles HOOKER and Spencer GRAVES. Functional Data Analysis with R and MATLAB. New York: Springer-Verlag New York, 2009. 202 pp. XII. ISBN 978-0-387-98184-0. info
- FERRATY, Frédéric and Philippe VIEU. Nonparametric functional data analysis : theory and practice. New York: Springer, 2006. xx, 258. ISBN 0387303693. info
- RAMSAY, J. O. and B. W. SILVERMAN. Applied functional data analysis : methods and case studies. New York: Springer, 2002. x, 190. ISBN 0387954147. info
- RAMSAY, J. O. and B. W. SILVERMAN. Functional data analysis. New York: Springer-Verlag, 1997. xiv, 310. ISBN 0387949569. info
- Teaching methods
- Computer exercises: real data analysis in R, simulations. An individual working on assignments. Preparation for a final group project.
- Assessment methods
- Assignments (50%) and a final project (50%). Students are expected to work individually on assignments. The project may be undertaken in small groups.
- Language of instruction
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/sci/autumn2021/M7777