SubjectsSubjects(version: 945)
Course, academic year 2016/2017
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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 2014 to 2016
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: 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. Mgr. Michal Kulich, Ph.D.
Class: M Mgr. FPM
M Mgr. FPM > Povinně volitelné
M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Pre-requisite : {Prerekvizita pro NMST531}, NMSA407
Is pre-requisite for: NMST532
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.

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)

Lecture+exercises.

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

1. Censored random variable.

2. Parametric models for censored data.

3. Nonparametric estimation of hazard and survival function.

4. Nonparametric two-sample tests.

5. Cox regression model.

6. Transformation models.

7. Diagnostics.

8. Repeated event analysis.

 
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