Introduction to Mathematical Statistics

WS 2021/22



Tuesday 10 a.m. - 12 a.m. (M2)
Friday 10 a.m. - 12 a.m. (M2)

Due to a scientific leave in the first week of the semester, the first two courses will be delivered online, presumably recorded and posted in advance on the Learnweb page of the course.

Dozent: Prof. Dr. Gerold Alsmeyer
Assistant: Konstantin Recke



Topics: Moving from Probability Theory to Mathematical Statistics, the major novelty is the decision-theoretic aspect that comes into play because the focus is shifted from the analysis of a given model to the purpose of "explaining" a given set of random data, which are based on an unknown probability model, by picking, in a suitable sense to be described, the most plausible model from a given set. This course aims at laying the foundations for this task. It will start by providing some basics of decision theory and then proceed to describe the main notions and approaches to solve prototypical statistical problems of increasing complexity. Here are some keywords: point estimation, maximum likelihood principle, unbiased estimation, method of moments, sufficiency and completeness, Lehman-Scheffé theorem, linear regression, Gauss-Markov theorem, hypothesis testing, level-α tests, Neyman-Pearson lemma, one- and two-sided hypotheses, Gauss tests, t-test (heuristic derivation).
Learnweb: Please enroll in the Learnweb course for the lecture. 
Tutorials: T.b.a. in the Learnweb course.