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Course, academic year 2023/2024
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Censored Data Analysis - NMST531
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: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: not taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Additional information:
Guarantor: doc. RNDr. Daniel Hlubinka, Ph.D.
Class: M Mgr. FPM
M Mgr. FPM > Povinně volitelné
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Pre-requisite : {Prerekvizita pro NMST531}, NMSA407
Is incompatible with: NMST511
Is interchangeable with: NMST511
Annotation -
Last update: G_M (28.05.2013)
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.
Aim of the course -
Last update: T_KPMS (07.05.2015)

To explain methods for censored data analysis.

Course completion requirements
Last update: doc. Mgr. Michal Kulich, Ph.D. (24.09.2020)

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 - Czech
Last update: T_KPMS (16.09.2014)

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 -
Last update: T_KPMS (12.05.2014)


Requirements to the exam
Last update: doc. Mgr. Michal Kulich, Ph.D. (24.09.2020)

The form of the exam will be determined later according to the SARS-CoV-2 prevalence at the time. Requirements for the oral comprise the entire extent of the lecture.

Syllabus -
Last update: doc. Mgr. Michal Kulich, Ph.D. (24.09.2020)

1. Censored random variable.

2. Parametric models for censored data.

2. Counting processes and martingales for censored data.

3. Nonparametric estimation of hazard and survival function.

4. Nonparametric two-sample tests.

5. Cox regression model.

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

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.

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