Censored Data Analysis - NMST511

• Title: Analýza censorovaných dat
• Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
• Faculty: Faculty of Mathematics and Physics
• Actual: from 2023
• Semester: winter
• E-Credits: 6
• Hours per week, examination: winter s.:3/2, 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/cens/index.html
• Guarantor: doc. RNDr. Daniel Hlubinka, Ph.D.
• Class: M Mgr. PMSE; M Mgr. PMSE > Povinně volitelné
• Classification: Mathematics > Probability and Statistics
• Incompatibility : NMST531
• Pre-requisite : NMSA405, NMSA407
• Interchangeability : NMST531
• Is pre-requisite for: NMST532

Annotation

English

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

The course connects probability theory (martingales), theoretical statistics (rank tests), reliability theory and survival theory. It will cover counting processes, survival function and hazard function estimates, parametric models, two- and k-sample tests for censored data, regression models. Practice sessions include theoretical exercises and practical applications.

Czech

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

Předmět propojuje teorii pravděpodobnosti (martingaly), teoretickou statistiku (pořadové testy), teorii spolehlivosti a analýzu přežití. Proberou se čítací procesy, odhady funkce přežití a kumulativního rizika, parametrické modely, dvou a vícevýběrové testy na censorovaná data, regresní modely. Cvičení obsahuje teoretické příklady i praktické aplikace.

Aim of the course

English

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

To explain methods for censored data analysis.

Czech

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

Seznámit studenty s metodami pro analýzu censorovaných dat.

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.

Requirements for exercise class credit: 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. Ing. Marek Omelka, Ph.D. (30.11.2020)

Fleming TR and Harrington DP "Counting Processes and Survival Analysis" Wiley, New York, 1991.

Kalbfleisch JD and Prentice RL "The Statistical Analysis of Failure Time Data". Wiley, New York, 2002.

Teaching methods

English

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

Lecture+exercises.

Czech

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

Přednáška + cvičení.

Syllabus

English

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

1. Censored random variable.

2. Parametric models for censored data.

3. Counting processes and martingales for censored data.

4. Nonparametric estimation of hazard and survival function.

5. Nonparametric two-sample tests.

6. Cox regression model.

Czech

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

1. Censorovaná náhodná veličina.

2. Parametrické modely pro censorovaná data.

3. Čítací procesy a martingaly pro censorovaná data.

4. Neparametrické odhady rizikové funkce a funkce přežití.

5. Neparametrické dvouvýběrové testy.

6. Coxův regresní model.

Entry requirements

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

This course assumes the knowledge of linear regression theory and, preferably but not necessarily, generalized linear models. Intermediate-level knowledge of probability theory, including continuous martingales, and counting process theory is also required.