**Prerequisites**

It is assumed that participants in the course have, at the least, some knowledge of the basic concepts in statistics: estimation, testing and confidence sets; the definitions of moment estimators, the maximum likelihood estimator, Bayes procedures, etc. Results and concepts from probability theory that need to be familiar: the law of large numbers and the central limit theorem; normal, exponential, gamma, binomial, poisson families of distributions etc. Furthermore, at least a passing familiarity with measure theory is extremely useful if not indispensable at the beginning of the course: concepts like sigma-algebras, measurable functions, measures, sigma-additivity, integration, monotone limits, etc, should not be wholly unknown. For those participants who feel under-equipped measure-theoretically, the (simultaneous) course in Measure Theoretic Probability is highly recommended.

**Aim of the course**

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

Some of the topics that will be treated

Modes of stochastic convergence, multivariate normal distributions, delta method, consistency and asymptotic normality of Z- and M-estimators, Glivenko-Cantelli.

**Homework/exam**

Written midterm exam (50%), written final exam (50%).

Retake is a single exam (100%)

**Lecture Notes/Literature**

Lecture notes will be provided.

Background literature: the book, "Asymptotic Statistics", by A. W. van der Vaart, Cambridge University press.

- Docent: Bas Kleijn