Poslední úprava: doc. RNDr. Zdeněk Hlávka, Ph.D. (22.09.2023)
Introduction to nonparametric, multivariate, and resampling methods. Statistical models, computational aspects, applications.
Poslední úprava: doc. RNDr. Zdeněk Hlávka, Ph.D. (22.09.2023)
Introduction to nonparametric, multivariate, and resampling methods. Statistical models, computational aspects, applications.
Literatura -
Poslední úprava: doc. RNDr. Zdeněk Hlávka, Ph.D. (02.10.2023)
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979) Multivariate analysis. Academic Press Inc.
Härdle, W. and Simar, L. (2015) Applied multivariate statistical analysis. Springer.
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap methods and their application. Cambridge University Press.
Efron, B. and Tibshirani, R. J. (1993) An Introduction to the bootstrap. Chapman & Hall.
Fan, J. and Gijbels, I. (1996) Local polynomial modelling and Its applications. Chapman & Hall/CRC.
Kulich, M. and Omelka M. (2023) NMSA331: Mathematical statistics 1. https://www2.karlin.mff.cuni.cz/~omelka/Soubory/nmsa331/ms1_en.pdf
Omelka, M. and Nagy S.. (2023) NMST544: Mathematical statistics 4. www.karlin.mff.cuni.cz/~nagy/NMST545/NMST545.pdf
Wand, M. P. and Jones, M. C. (1995) Kernel smoothing. Chapman & Hall.
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (08.02.2024)
Härdle, W. and Simar, L. (2015) Applied multivariate statistical analysis. Springer.
Kulich, M. and Omelka M. (2023) NMSA331: Mathematical statistics 1. https://www2.karlin.mff.cuni.cz/~omelka/Soubory/nmsa331/ms1_en.pdf
Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979) Multivariate analysis. Academic Press Inc.
Nagy S.. (2023) NMSA332: Mathematical statistics 2. https://www.karlin.mff.cuni.cz/~nagy/NMSA332/NMSA332.pdf
Omelka, M. (2023) NMST424: Modern statistical methods. https://www.karlin.mff.cuni.cz/~omelka/Soubory/nmst434/nmst434_course-notes.pdf
Wasserman, L. (2006). All of nonparametric statistics. Springer Science & Business Media.
Further recommended reading:
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap methods and their application. Cambridge University Press.
Efron, B. and Tibshirani, R. J. (1993) An Introduction to the bootstrap. Chapman & Hall.
Fan, J. and Gijbels, I. (1996) Local polynomial modelling and Its applications. Chapman & Hall/CRC.
Wand, M. P. and Jones, M. C. (1995) Kernel smoothing. Chapman & Hall.
Požadavky ke zkoušce -
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (16.05.2024)
(1) Written assignment (max 40 points) in which the student analyses real data set and solves a theoretical problem. The assignment has to be submitted at least 2 working days before the oral exam and will be graded by the examiner.
(2) Oral exam (max 60 points) focusing on all topics of the course, with an emphasis on the theoretical part and correct understanding. Three slots for oral exams will be available during the examination period May-June.
You need at least 51 points in total to pass the course. The final grade will be awarded based on your total number of points using the official faculty grading system, i.e.:
91– 100 pts Excellent A 81 – 90 pts Very good B 71 – 80 pts Good C 61 – 70 pts Satisfactory D 51 – 60 pts Satisfactory- E 0 – 50 pts Failed F
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (16.05.2024)
(1) Written assignment (max 40 points) in which the student analyses real data set and solves a theoretical problem. The assignment has to be submitted at least 2 working days before the oral exam and will be graded by the examiner.
(2) Oral exam (max 60 points) focusing on all topics of the course, with an emphasis on the theoretical part and correct understanding. Three slots for oral exams will be available during the examination period May-June.
You need at least 51 points in total to pass the course. The final grade will be awarded based on your total number of points using the official faculty grading system, i.e.:
91– 100 pts Excellent A 81 – 90 pts Very good B 71 – 80 pts Good C 61 – 70 pts Satisfactory D 51 – 60 pts Satisfactory- E 0 – 50 pts Failed F
Sylabus -
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (08.02.2024)
Basic notions, t-tests and rank tests.
Resampling methods.
Nonparametric regression: kernel estimators of densities and regression curves.
Multivariate statistical methods I: random vectors, multivariate normal distribution, Hotelling's test, multiple testing.
Multivariate statistical methods II: principal components, factor analysis, discriminant and cluster analysis, further dimension reduction methods.
Other topics of interest
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (21.04.2024)
Basic notions, t-tests and rank tests.
Resampling methods.
Nonparametric regression: kernel estimators of densities and regression curves.
Multivariate statistical methods I: random vectors, multivariate normal distribution, Hotelling's test, multiple testing.
Multivariate statistical methods II: principal components, factor analysis, discriminant and cluster analysis, further dimension reduction methods.
Other computational procedures.
Vstupní požadavky - angličtina
Poslední úprava: doc. Ing. Marek Omelka, Ph.D. (21.02.2024)
Basic courses in probability and mathematical statistics.
At least elementary knowledge of R computing environment (https://cran.r-project.org/).
