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## Asymptotic Statistics

### Summary

EC | 8 |

Location | University of Amsterdam |

Weeks | 37 - 51 |

Lecture | Wednesday, 10:00 - 12:45 |

Provider | Stochastics (Star) |

Links | Course page (requires login) |

**[Fall 2020]**

**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.

**Lecturers**

Harry van Zanten