M7777 Applied functional data analysis

Faculty of Science
autumn 2021
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.
The course is also listed under the following terms Autumn 2019, Autumn 2020.
  • Enrolment Statistics (recent)
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