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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

Mathematical Statistics

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Code Completion Credits Range Language
01MAS ZK 3 2+0 Czech
Lecturer:
Václav Kůs (guarantor)
Tutor:
Supervisor:
Department of Mathematics
Synopsis:

The subject is devoted to usage of statistical methods studied in the course of Mathematical statistics. We deal with Fisher information matrix of statistical models, finding unbiased estimators with minimal variance, parameter estimation by method of moments and method of maximum likelihood, derivation of critical regions for hypothesis testing using the Neyman-Pearson lemma and likelihood ratio, confidence intervals and non-parametric density estimation.

Requirements:

01MIP nebo 01PRST

Syllabus of lectures:

1. Unbiased minimum variance estimates, Fisher information matrix, Rao-Cramér inequality, Bhattacharrya inequality.

2. Moment estimators, Maximum likelihood principle, consistency, asymptotic normality and efficiency of MLE.

3. Testing of simple and composite hypotheses. The Neyman-Pearson lemma.

4. Uniformly most powerful tests. Randomized testing, generalized Neyman-Pearson lemma.

5. The likelihood ratio test, t-test, F-test.

6. Nonparametric models, empirical distribution and density function, their properties.

7. Histogram and kernel density estimates (adaptive), properties.

8. Pearson goodness of fit test, Kolmogorov-Smirnov test.

9. Confidence sets and intervals, pivotal quantities, acceptance regions, Pratt theorem.

Syllabus of tutorials:
Study Objective:

Knowledge:

In frame of the course, to provide students with the knowledge necessary for the following future subjects using stochastic models. To give a deeper insight into the field in the area of point statistical parameter estimation and testing statistical hypothesis in parametric and nonparametric probabilistic models.

Skills:

Basic statistical models and testing hypotheses processing. Orientation in majority of standard notions of the statistics and capabilities of practical applications in actual stochastic computations. Statistical data processing in statistical parametric and nonparametric model estimation and testing.

Study materials:

Key references:

[1] Shao J., Mathematical Statistics, Springer, 1999.

Recommended references:

[2] Schervish M.J., Theory of Statistics, Springer, 1995.

[3] Lehmann E.L., Point Estimation, Wiley, N.Y., 1984.

[4] Lehmann E.L., Testing Statistical Hypotheses, Springer, N.Y., 1986.

Note:
Time-table for winter semester 2019/2020:
Time-table is not available yet
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-05-30
For updated information see http://bilakniha.cvut.cz/en/predmet5358606.html