Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Generalised Linear Models - NSTP196

Title:	Zobecněné lineární modely
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2013 to 2017
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	http://www.karlin.mff.cuni.cz/~kulich/vyuka/glm/index.html

Guarantor:	doc. Mgr. Michal Kulich, Ph.D.
Classification:	Mathematics > Probability and Statistics
Co-requisite :	NSTP202
Pre-requisite :	NSTP194, NSTP201
Is co-requisite for:	NSTP197
Is incompatible with:	NSTP126

Opinion survey results Examination dates Schedule Noticeboard

Annotation -

Last update: T_KPMS (19.03.2008)

Generalized linear models. Maximum likelihood theory. Exponential families of distributions. Logistic regression. Poisson regression. Loglinear models. Overdispersion. Quasilikelihood.

Aim of the course -

Last update: T_KPMS (20.05.2008)

Understand principles of parameter estimation and hypotheses testing

in a generalized linear model, apply the methods to build models to

address practical objectives, learn to interpret the results properly.

Literature - Czech

Last update: T_KPMS (19.03.2008)

Mc Cullagh, P. and Nelder, J.A.: Generalized Linear Models. 2nd Ed. Chapman and Hall, London, 1989.

Dobson, A.J.: An Introduction to Generalized Linear Models. CRC Press, Boca Raton, 1999.

Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S. 4th Ed. Springer, New York, 2002.

Teaching methods -

Last update: G_M (27.05.2008)

Lecture.

Syllabus -

Last update: T_KPMS (19.03.2008)

Generalized linear models. Maximum likelihood theory. Exponential families of distributions. Logistic regression. Poisson regression. Loglinear models. Overdispersion. Quasilikelihood.