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

English

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.

Czech

Last update: doc. RNDr. Martin Branda, Ph.D. (11.12.2020)

Předmět rozšiřuje základy lineární regrese. Učí se zde regresní modely pro nenormální data a diskrétní rozdělení. Cvičení je kombinací teoretických a praktických úloh, ale jádro spočívá v analýzách různých typů ekonometrických, finančních a technických dat a zahrnuje závěrečný projekt.

Aim of the course

English

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

To explain regression models for non-normal data.

Czech

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

Seznámit studenty s regresními modely pro nenormální 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

English

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.

Czech

Last update: doc. RNDr. Martin Branda, 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

English

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

Lecture + exercises.

Czech

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

Přednáška + cvičení.

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

English

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

Czech

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

1. Zobecněný lineární model

2. Regrese pro binární data

3. Loglineární model

4. Rozšíření zobecněného lineárního modelu, kvazivěrohodnost, sandwichové odhady rozptylu

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.