Knowledge about theoretical foundations and methods of mathematical statistics
Wymagania wstępne
Mathematical analysis and probability theory
Zakres tematyczny
Lecture:
Normal (Gaussian) probability distribution and probability distribustions related to the normal one.
Random variables and their basic probabilistic characteristics, Random variable with Gaussian distribution (2 h)
Chi-square distribution, t-Student's distribution, and F-Snedecor's distribution (1 h)
Statistical Models.
Aims of statistical analysis, statistical space, statistical sample, limit theorems for the empirical distribution function (3 h)
Probability distributions of selected sample statistics, Fisher's theorem (2 h)
Sufficient statistics, factorization theorem, completeness of statistics (4 h)
The family of exponential distributions, space of parameters, Lehmann's theorem (2 h)
Statistical Hypotheses.
Basic notions(2 h)
Uniformly most powerful tests, Neyman-Pearson's Lemma (3 h)
Uniformly most powerful test in models with a monotonic likelihood ratio, Karlin Rubin's theorem (2h)
Classes
Repetition of elements of probability theory. Normal distribution and its properties. Statistical tables. Distributions of random vectors, multivariate normal distribution, and its characteristics. Functions of random variables and their distributions (2 h)
Independence. The notion of the statistical sample and its distribution. Applications of Fisher's theorem (3 h)
Conditional distributions. Calculations of sufficient statistics. Applications of factorization theorem for evaluations of sufficient statistics (3 h)
Examples of exponential families of distributions and applications of Lehmann's theorem for evaluations of sufficient and complete statistics (3 h)
Estimators-biased and unbiased. Calculations of mean and variance of selected estimators (1 h)
Control work (2 h)
Applications of the Lehmann-Sheffe Thm and Rao-Blackwell Thm for constructions of unbiased and with minimal variance estimators (2 h)
The method of moments and the maximal likelihood method in constructions of selected parameters estimators (3 h)
Confidence intervals and the real data analysis (4 h)
Testing statistical hypotheses. Probabilities of Type I and Type II errors. Power test function (2 h)
Uniformly most powerful tests-practical exercises (3 h)
Control work (2 h)
Metody kształcenia
Lectures
Classes: exercises (theoretical and computational)
Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się
Opis efektu
Symbole efektów
Metody weryfikacji
Forma zajęć
Warunki zaliczenia
Students' activities during classes, tests with exercises, exam
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