Measure theory and asymptotic statistics
2018-2019 (TI1708)
Tinbergen Institute

Aim

To make students familiar with the mathematical fundamentals of measure theory and asymptotic methods in statistics. This is a crash course, highlighting the main principles, not an in depth treatment of the theory.

Contents

Part I: Sigma-algebras, measure, integration w.r.t. a measure, limit theorems, product measure and integration, change of measure, conditional expectation. Part II: Multivariate central limit theorem, quadratic forms, delta-method, moment estimators, Z-and M-estimators, consistency and asymptotic normality, maximum likelihood estimators.

Literature

The first weeks of the course are mainly based on the first chapters of the lecture notes Measure theoretic probability; in particular we will treat parts of the Chapter 1,3,4,5,6 and 8, not everything. For this part also the first chapters in Steve Shreve (2004), Stochastic Calculus for Finance II, Continuous-Time Models can be useful. This book is written with a wide range of applications in Mathematical Finance in mind, but will not be used. The second part of the course will be based on the lecture notes Mathematische Statistiek by A.W. van der Vaart (title and preface are in Dutch, content in English).
New is a short version of the lecture notes on Measure theoretic probability, that contains all material treated in the lectures of this course (and a little more for the sake of completeness). The numbering in the first Programme below refers to the full version of the lecture notes, the alternative second part for the first three weeks refers to the numbering of the short set of lecture notes.

Examination

We will follow the usual conventions for TI core courses, i.e. there will be a written closed book exam and homework assignments. Homework has to be handed every week to the ASSISTANT. Your final grade F will be a weighted average of your result H of the homework assignments and the result E of the written exam: F=0.85*E+0.15*H. Homework assignments may be made in pairs (at most TWO people). The exercises at the exam will be at the level of the homework and tutorial sessions, but may also contain some theory. You don't have to know all proofs by heart, but at least the gist of them. Important theorems and definitions you are required to know. The written exam is on October 24, 13:30-16:30. During the exam you are allowed to use printed copies of the two sets of lecture notes.
This course is for a major part on new topics. Measure theory was also part of an older course, and on this topic there are a number of old exam questions available.

People

Lectures by Peter Spreij, teaching assistance by Alejandro Hirmas Frisius.

Schedule

Fall semester, 1st half. Lectures mostly on Wednesdays, 14:30-17:15; first lecture on 5 September 2018, 13:30-16:15; fourth lecture on Thursday 20 September, 10:30-13:15, following the third lecture on Wednesday 19 September. Tutorials before the lectures, starting Wednesday 12 September 2018, 09:30; last session to be decided.

Location

Tinbergen Institute Amsterdam, Gustav Mahlerplein 117, 1082 MS Amsterdam

Programme
(please, look out for updates; )

1
Class: From Spreij, Sections 1.1 (skip the cardinality part), 1.2, 1.3, (most of) 1.4, Most of 3.1 (ignore everything on topology), 3.2 (skip the proof of Theorem 3.10), 3.3 (up to Corollary 3.13) .
Tutorial: Make Exercises 1.6, 1.9, 3.2, 3.4.
Homework: Make Exercises 1.1, 1.2, 1.4, 3.1; Read Section 1.5, Proposition 3.12 with the proof and the statement of Theorem 3.6.
2
Class: Most of Sections 4.1 (some tedious proofs will be skipped), 4.2 (without Lemma 4.20), 4.4 (without Propositions 4.30, 4.31), 4.6 (up to Proposition 4.42), 5.1 up to Theorem 5.5 (ignore Remark 5.2), very brief mentioning of Section 5.2.
Tutorial: Make Exercises 4.3, 4.4, 4.9, 5.4.
Homework: Make Exercises 4.10, 4.12, 5.5, 5.6. Have a glance at the main issues of Section 5.2.
3
Class: Section 6.4: Proposition 6.8 only. Take all "measures" in this chapter as the usual positive measures! Section 6.5 (without proofs); Chapter 8: introduction, Most of Section 8.1 without Theorem 8.6 and Example 8.8 and not all proofs. General idea of Section 8.2.
Tutorial: Make Exercises 6.4, 6.7, 8.1, 8.6 .
Homework: Make Exercises 6.6 (optional), 6.9, 8.3.
4
Class: Chapter 1 of Van der Vaart, and most parts of Sections 2.1 and 2.2. Chapter 6 (Appendix) is supposed to be known.
Tutorial: Chapter 1: Exercises 1, 6, 7, 15, 21.
Homework: Chapter 1: Exercises 12, 25; and optional: 19, 20a, 22.
5
Class: Sections 2.3, 2.4, first part of Section 2.5; Sections 3.1, 3.3, perhaps Section 3.2.
Tutorial: Chapter 2: Exercises 9+10, 11; Chapter 3: 4, 20.
Homework: Chapter 2: Exercises 3, 4; Chapter 3: 1, 11, 12.
6
Class: Sections 4 and 4.1.
Tutorial: Chapter 4: Exercises 1, 2, 5.
Homework: Chapter 4: Exercises 3, 6, 7.
7
Class: Sections 4.2 (but not Subsection 4.2.1) and 4.3.
Tutorial: Chapter 4: Exercises 4, 12, 13, 21.
Homework (optional!): Chapter 4: Exercises 10, 11(i,ii), 19, 23.

Programme first three weeks with numbering referring to the short set of lecture notes

1
Class: From Spreij, Sections 1.1, 1.2, 1.3, (most of) 1.4, Most of 2.1 (ignore everything on topology), 2.2 (skip the proof of Theorem 2.10), 2.3 (up to Corollary 3.13) .
Tutorial: Make Exercises 1.6, 1.9, 2.2, 2.4.
Homework: Make Exercises 1.1, 1.2, 1.4, 2.1; Read Section 1.5, Proposition 2.12 with the proof and the statement of Theorem 2.6.
2
Class: Most of Sections 3.1 (some tedious proofs will be skipped), 3.2, 3.4 (without Propositions 3.29, 3.30), 3.5 (up to Proposition 3.34), 4.1 up to Theorem 4.5, very brief mentioning of Section 4.2.
Tutorial: Make Exercises 3.3, 3.4, 3.9, 4.4.
Homework: Make Exercises 3.10, 3.12, 4.5, 4.6. Have a glance at the main issues of Section 4.2.
3
Class: Section 5.1: mainly Proposition 5.3 only, Section 5.2; Section 6.1, most of Section 6.2 without Theorem 6.7 (not all proofs). General idea of Section 6.3.
Tutorial: Make Exercises 5.1, 5.4, 6.1, 6.6.
Homework: Make Exercises 5.3 (optional), 5.5, 6.3.





Links

Korteweg-de Vries Institute for Mathematics
Master Stochastics and Financial Mathematics