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Advanced Regression Models - NMST432

Title:	Pokročilé regresní modely
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2013 to 2019
Semester:	summer
E-Credits:	8
Hours per week, examination:	summer s.:4/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	http://www.karlin.mff.cuni.cz/~kulich/vyuka/pokreg/index.html

Guarantor:	doc. Mgr. Michal Kulich, Ph.D.
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Mathematics > Probability and Statistics
Pre-requisite :	NMSA407
Is pre-requisite for:	NMST532, NMST436, NMST551, NMST552

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: G_M (28.05.2013)

Continuation of the course NMSA407 Linear Regression. This course covers regression models for non-normal data, discrete distributions, and clustered data. The practice sessions include solutions to theoretical excercises but the focus is on analyses of different types of econometric, medical and technical data. The course is concluded by a Final Project.

Aim of the course -

Last update: T_KPMS (07.05.2015)

To explain regression models for non-normal and/or correlated data.

Literature - Czech

Last update: T_KPMS (12.05.2014)

J.W. Hardin and J.M. Hilbe: Generalized Linear Model and Extensions. StataPress, 2007.

A. Agresti: Categorical Data Analysis. Wiley, 1990.

J.W. Hardin and J.M. Hilbe: Generalized Estimating Equations. Chapman & Hall, 2003.

P.J. Diggle, K.Y. Liang, S.L. Zeger: Analysis of Longitudinal Data. Oxford University Press, 1994.

Teaching methods -

Last update: T_KPMS (12.05.2014)

Lecture+exercises.

Syllabus -

Last update: doc. Mgr. Michal Kulich, Ph.D. (04.02.2018)

1. Generalized linear model

2. Binary response regression

3. Loglinear model

4. Extensions of generalized linear model

5. Generalized linear model for continuous data

6. Multinomial and ordinal responses

7. Generalized estimating equations

8. Linear mixed model

9. Generalized linear mixed model