Learn to study statistical procedures from an asymptotic point of view.

The course starts with a review of various concepts of stochastic convergence (e.g. convergence in probability or in distribution) and properties of the multivariate normal distribution. Then the asymptotic properties of various statistical procedures are studies, including Chi-square tests, Moment estimators, M-estimators (including MLE) and Kernel density estimators. The examples are chosen according to importance in practical applications, and the theory is motivated by practical relevance, but the subjects are presented in theorem-proof form.

Prerequisites

General statistics and preferably also Measure Theory.