SubjectsSubjects(version: 953)
Course, academic year 2023/2024
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Generalized Linear Models - NMST412
Title: Zobecněné lineární modely
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: English, 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.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Incompatibility : NMFP402, NMST432
Pre-requisite : NMSA407
Interchangeability : NMST432
Is incompatible with: NMFP402
Is pre-requisite for: NMST551, NMEK521, NMST532, NMST552
Is interchangeable with: NMFP402
In complex pre-requisite: NMST539, NMST547
Annotation -
Continuation of the course NMSA407 Linear Regression. This course covers regression models for non-normal data and discrete distributions. 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.
Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
Aim of the course -

To explain regression models for non-normal data.

Last update: Omelka Marek, doc. Ing., Ph.D. (02.12.2020)
Course completion requirements

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.

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

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

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

Last update: Kulich Michal, doc. Mgr., Ph.D. (27.01.2023)
Teaching methods -

Lecture+exercises.

Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
Requirements to the exam

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.

Last update: Kulich Michal, doc. Mgr., Ph.D. (27.01.2023)
Syllabus -

1. Generalized linear model

2. Binary response regression

3. Loglinear model

4. Extensions of generalized linear model, quasilikelihood, sandwich estimator of variance

Last update: Omelka Marek, doc. Ing., Ph.D. (01.12.2020)
Entry requirements

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

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