Prof Umut Cetin

This course is available on the MSc in Quantitative Methods for Risk Management. This course is available with permission as an outside option to students on other programmes where regulations permit.

The availability as an outside option requires a demonstration of sufficient background in mathematics and statistics. Prior training on basic concepts of real analysis providing experience with formal proofs, sequences, continuity of functions, and calculus and is at the discretion of the instructor.

This course provides theoretical and axiomatic foundations of probability and mathematical statistics. In particular, the following topics will be covered:

1. Measure spaces; Caratheodory extension theorem; Borel-Cantelli lemmas.

2. Random variables; monotone-class theorem; different kinds of convergence.

3. Kolmogorov’s 0-1 law; construction of Lebesgue integral.

4. Monotone convergence theorem; Fatou's lemmas; dominated convergence theorem.

5. Expectation; L^p spaces; uniform integrability.

6. Characteristic functions; Levy inversion formula; Levy convergence theorem; CLT.

7. Principle and basis for statistical inference: populations and samples, decision theory, basic

measures for estimators.

8. Estimation: U and V statistics, unbiased estimators, MVUE, MLE.

9. Hypothesis testing: Neyman-Pearson lemma, UMP, confidence sets.

10. Product measures; conditional expectation.

This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours across Michaelmas Term. This year, some of this teaching may be delivered through a combination of classes and flipped-lectures delivered as short online videos.This course includes a reading week in Week 6 of Michaelmas Term.

Students will be expected to produce 9 problem sets in the MT.

Weekly problem sets that are discussed in subsequent seminars. The coursework that will be used for summative assessment will be chosen from a subset of these problems.

1. Williams, D. (1991). Probability with Martingales. Cambridge University Press.
2. Durrett, R. (2019). Probability: Theory and Examples. Cambridge Series in Statistical and Probabilistic Mathematics.
3. Shao, J. (2007). Mathematical Statistics. Springer Texts in Statistics.
4. Keener, R. (2010). Theoretical Statistics. Springer Texts in Statistics.

Exam (70%, duration: 2 hours, reading time: 10 minutes) in the summer exam period.
Coursework (30%) in the MT.

Three of the homework problem sets will be submitted and marked as assessed coursework.

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.