**Requirements/prerequisites:**

**Duration:** Michealmas, Hilary and Trinity

**Number of lectures per week:**

**Assessment:**

**End-of-year Examination:**

**Description: **

Basic Concepts of Inference.

Estimators, Mean squared Error, Sufficiency, MVUEs. Maximum
likelihood estimation. Properties of maximum likelihood. Bayesian
estimation.

Hypothesis testing, likelihood ratio tests. Bayesian methods. Other
approaches: nonparametrics, order statistics, monte carlo methods -
robustness.

The linear model. Least Squares estimates. BLUEs.

Extending the scope of the linear model: transformations, Box-Cox,
Weighted regression. General and generalised linear models.

A Bayesian approach to inference.

**Textbooks:**

Silvey, S.D., ``Statistical Inference".

McCullagh and Nelder, ``Generalised Linear Models".

Lee, P., ``Bayesian Statistics: An Introduction".

Jun 10, 1998