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Lecturer(s)
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Pavlů Ivana, Mgr. Ph.D.
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Hron Karel, prof. RNDr. Ph.D.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Course content
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1. From multivariate to functional data 2. Spline representation of functional data 3. Exploratory analysis, FPCA 4. Regression with functional explanatory variable 5. Regression with functional response 6. Functional time series 7. Spatial functional data
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Learning activities and teaching methods
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Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
- Preparation for the Course Credit
- 20 hours per semester
- Attendace
- 52 hours per semester
- Preparation for the Exam
- 40 hours per semester
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Learning outcomes
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Understand popular methods of functional data analysis including their implementation in statistical software R.
Application Apply probability theory and mathematical statistics to methods of functional data analysis.
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Prerequisites
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Basic knowledge of probability theory and multivariate statistics.
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Assessment methods and criteria
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Oral exam, Seminar Work
Credit: Presetation of a project covering topics of statistical learning. Exam: oral.
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Recommended literature
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James, G., Witten, D., Hastie, T., Tibshirani, R. (2021). An introduction to statistical learning. An introduction to statistical learning.
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Kokoszka, P., Reimherr, M. (2017). Introduction to functional data analysis. CRC Press, Boca Raton.
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Ramsay, J.O., Hooker, G., Graves, S. (2009). Functional data analysis with R and MATLAB. Springer, New York.
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Ramsay, J.O., Silverman, B.W. (2022). Applied functional data analysis. Springer, New York.
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Ramsay, J.O., Silverman, B.W. (2005). Functional data analysis. Springer, New York.
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