SubjectsSubjects(version: 942)
Course, academic year 2023/2024
   Login via CAS
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 2022
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: not taught
Language: English, Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information:
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 incompatible with: NMST422, NMST412, NMFP402
Is interchangeable with: NMST422, NMST412
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.

Course completion requirements
Last update: RNDr. Jitka Zichová, Dr. (23.04.2018)

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: 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)


Requirements to the exam
Last update: doc. Mgr. Michal Kulich, Ph.D. (30.04.2020)

Only for summer 2020: if conduct of an oral exam is impossible, oral part can be performed by distant methods or waived.

The exam has two parts: (1) Evaluation of applied project report and (2) Theoretical oral part. To pass the exam, both parts need to be passed.

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

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, quasilikelihood, sandwich estimator of variance

5. Generalized estimating equations

6. Linear mixed effects model

7. Generalized linear mixed effects model

Entry requirements
Last update: doc. Mgr. Michal Kulich, Ph.D. (25.05.2018)

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

Charles University | Information system of Charles University |