Subjects

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

Longitudinal and Panel data - NMST422

Title:	Longitudinální a panelová data
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2023
Semester:	summer
E-Credits:	5
Hours per week, examination:	summer s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	doc. RNDr. Zdeněk Hlávka, Ph.D. doc. RNDr. Michal Pešta, Ph.D.
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Mathematics > Probability and Statistics
Incompatibility :	NMST432
Pre-requisite :	NMSA407
Interchangeability :	NMST432
Is pre-requisite for:	NMST552, NMST551

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)

Continuation of the course NMSA407 Linear Regression. This course covers regression models for 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: doc. Ing. Marek Omelka, Ph.D. (10.06.2021)

To explain regression models for correlated and clustered data.

Course completion requirements -

Last update: doc. Ing. Marek Omelka, Ph.D. (30.11.2020)

The exercise class credit is necessary to sign up for the exam. The credit for the exercise class will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline. The nature of these requirements precludes any possibility of additional attempts to obtain the exercise class credit.

Literature - Czech

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (23.01.2023)

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

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

P.J. Diggle, P.J. Heagerty, K.-Y. Liang, S.L. Zeger: Analysis of Longitudinal Data. Oxford University Press, 2nd edition, 2002.

Teaching methods -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (26.01.2023)

Lecture + exercises. The faculty computing cluster can be used for more demanding data analyses and processing of large data sets.

Requirements to the exam -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (23.01.2023)

Requirements for the oral exam comprise the entire contents of the lectures and exercise sessions.

Syllabus -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (23.01.2023)

Linear mixed effects model; Generalized linear mixed effects model; Generalized estimating equations.

Entry requirements -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (23.01.2023)

This course assumes mid-level knowledge of linear regression (both theory and applications) and good understanding of maximum likelihood theory. Knowledge of generalized linear models is recommended.