In this course we study the basics of theoretical statistics. The course includes methods for designing estimators, confidence intervals and tests, and various ways to evaluate the accuracy of estimators, confidence intervals and tests. We consider optimality criteria such as admissibility and minimaxity, as well as Bayesian criteria. We will also present the asymptotic point of view.
Learning objective
The aim of this course is to gain insight into the main statistical ideas and concepts. The course considers classical low-dimensional models, with pointers towards today's highly complex models.
Competencies
Subject-specific Competencies
Concepts and Theories
assessed
Method-specific Competencies
Analytical Competencies
assessed
Problem-solving
assessed
Personal Competencies
Creative Thinking
assessed
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course