Subject description
Order Statistics.
Sufficiency and completeness.
Point estimation.
Hypothesis testing.
Sequential procedures.
Confidence regions.
Least square estimators.
Analysis of variance.
Nonparametric inference.
Introduction to Bayesian Statistics.
The subject is taught in programs
Objectives and competences
Students get acquainted with advanced aspects of mathematical statistics.
Teaching and learning methods
Lectures, exercises, homeworks, projects, self-study of literature, consultations.
Expected study results
Knowledge and understanding: Understanding of the notion »statistical model« and of the mathematical background of modelling, estimation and testing of statistical models.
Application: Statistics is one of the most applicable areas of mathematics. The projects will prepare students for application of statistics on all relevant areas.
Reflexion: Interplay between application, statistical modelling, feedback from other sciences and the encouragement for mathematical reasoning inspired by application.
Transferrable skills: Skills are transferrable to other areas of mathematical modelling. The course is especially important because of its immediate applicability.
Basic sources and literature
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G. G. Roussas. A course in mathematical statistics. Academic Press, 3rd edition, 2014.
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A. Gelman, J.B.Carlin, H.S. Stern, D.B. Rubin: Bayesian Data Analysis. 2nd edition, Chapman&Hall, 1995.
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