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Last update: T_KPMS (16.05.2013)
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Last update: doc. Ing. Marek Omelka, Ph.D. (11.04.2018)
Understand principles of advanced methods of statistical inference that are used in data analysis.
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Last update: doc. Ing. Marek Omelka, Ph.D. (11.04.2018)
FAN, J. and GIJBELS, I.: Local Polynomial Modelling and Its Applications. Chapman & Hall/CRC, London, 1996
LEHMANN, E. L. and CASSELLA, G. (1998). Theory of point estimation. Springer, New York.
MCLACHLAN, G. J., KRISHNAN, T.: The EM Algorithms and Extensions, Wiley, 2008
WAND, M. P. and JONES, M. C.: Kernel Smoothing. Chapman & Hall, 1995
SHAO, J. and TU, D.: The jackknife and bootstrap. Springer, New York, 1996.
Additional supporting literature: KOENKER, R.: Quantile regression. Cambridge university press, 2005.
LITTLE, R.J.A., RUBIN, D.B.: Statistical analysis with missing data. New York: John Wiley & Sons, 1987
PAWITAN, Y.: In all likelihood: statistical modelling and inference using likelihood. Oxford University Press, 2001.
SERFLING, R. J.: Approximation Theorems of Mathematical Statistics, Wiley, 1980.
VAN DER VAART, A. W. Asymptotic statistics. Cambridge university press, 2000. |
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Last update: T_KPMS (16.05.2013)
Lecture+exercises. |
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Last update: doc. Ing. Marek Omelka, Ph.D. (22.02.2018)
Clipping from the asymptotic theory - Delta Theorem
Theory of maximum likelihood
Profile, conditional and marginal likelihood
M-estimators and Z-estimators
Quasi-likelihood
Quantile regression
Kernel density estimation
Kernel nonparametric regression
Bootstrap
EM-algorithm
Methods for missing data
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