SubjectsSubjects(version: 944)
Course, academic year 2023/2024
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Generalized Linear Models - NMFP402
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
Is provided by: NMST412
Guarantor: doc. Mgr. Michal Kulich, Ph.D.
doc. RNDr. Martin Branda, Ph.D.
Class: M Mgr. FPM
M Mgr. FPM > Povinné
Classification: Mathematics > Financial and Insurance Math.
Incompatibility : NMST412, NMST432
Pre-requisite : NMFP401
Interchangeability : NMST412
Is incompatible with: NMST412
In complex pre-requisite: NMST539, NMST547
Annotation -
Last update: doc. RNDr. Martin Branda, Ph.D. (11.12.2020)
This course extends the foundations of linear regression. It covers regression models for non-normal data and discrete responses. The practice sessions combine theoretical and practical exercises with main focus on analyses of various types of econometric, financial and technical data. The course includes a final project.
Aim of the course -
Last update: doc. Mgr. Michal Kulich, Ph.D. (11.12.2020)

To explain regression models for non-normal data.

Course completion requirements
Last update: RNDr. Jitka Zichová, Dr. (20.05.2022)

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 -
Last update: doc. Mgr. Michal Kulich, Ph.D. (11.12.2020)

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

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

Teaching methods -
Last update: RNDr. Jitka Zichová, Dr. (20.05.2022)

Lecture + exercises.

Requirements to the exam
Last update: doc. Mgr. Michal Kulich, Ph.D. (11.12.2020)

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. (11.12.2020)

1. Generalized linear model

2. Binary response regression

3. Loglinear model

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

Entry requirements
Last update: doc. Mgr. Michal Kulich, Ph.D. (11.12.2020)

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

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