SubjectsSubjects(version: 953)
Course, academic year 2023/2024
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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:
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 -
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.
Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
Aim of the course -

To explain methods for censored data analysis.

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

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.

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

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.

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


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

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.

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

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.

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