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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)
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To explain methods for censored data analysis. Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
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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)
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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)
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Lecture+exercises. Last update: Omelka Marek, doc. Ing., Ph.D. (30.11.2020)
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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)
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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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