| Unit name | Statistical Inference |
|---|---|
| Unit code | MATHM6009 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Didelez |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
This unit will cover the principles and systematic structure of statistical inference, in a way that enables students to understand the uncertainty involved in the conclusions of statistical analyses, and assess the relative merits of different frameworks for statistical analysis. The unit is aimed at students who already have a basic knowledge of main components of statistical inference but wish to develop a deeper understanding of the relationship of these elements within different inference paradigms. Key ideas about probability models and the objectives of statistical analysis are introduced and the differences between the Bayesian and frequentist analyses are illustrated. The topics covered may include: Likelihood, sufficiency, ancillarity, conditionality and the fundamentals of exponential families. Statistical decision theory, minimax and Bayes rules, admissibility, Stein's paradox, hypothesis testing as a decision problem. Subjective and frequency interpretation of probability, the Bayesian paradigm, DeFinetti's theorem, conjugate and reference priors, empirical Bayes and related methods. Model comparison, significance testing and structural uncertainty.
