MATH6153 Statistical Theory and Linear Models
Module Overview
This module provides an intensive introduction to, or revision of, essential ideas in developing statistical theory. Firstly, basic statistical models are reviewed along with their properties. Then the transformation method for random variables is introduced to derive standard statistical distributions. The middle part of the module concerns the theory for making statistical inference, including methods such as maximum likelihood estimation and likelihood ratio tests, and an introduction to Bayesian methods. Finally, the theory of linear models is introduced, which will include multiple linear regression models and mixed effects models as examples
Aims and Objectives
Module Aims
• Standard univariate statistical models and their properties. • Theory of estimation and significance testing, and know when particular tests should be applied. • Bayesian inference methods for conjugate priors, prediction method and marginal likelihood. • Theory of linear statistical models.
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- A good understanding of standard univariate statistical models and their properties. A good understanding of the theory of estimation and significance testing, and know when particular tests should be applied. A good understanding of Bayesian inference methods for conjugate prior, prediction method and marginal likelihood. A good understanding of the theory of linear models.
Syllabus
• Univariate distributions: Common standard distributions and their properties. • Estimation: Unbiasedness, Method of Moments, • Likelihood - score functions, information, maximum likelihood estimators, Cramer-Rao • Inequality. • Confidence intervals: Asymptotic methods and interpretations. • Hypothesis testing: Neyman-Pearson Lemma and the Generalised likelihood ratio tests. • Bayesian methods for conjugate priors, prediction method and marginal likelihood. • Theory of linear models: o Simple and multiple linear regression, o The principle of least squares and least squares estimators, o Linear hypothesis testing, o Properties of least squares estimators, o Model selection.
Learning and Teaching
Teaching and learning methods
36 Lectures and 12 Tutorials
| Type | Hours |
|---|---|
| Teaching | 48 |
| Independent Study | 102 |
| Total study time | 150 |
Resources & Reading list
MH DeGroot & MJ Schervish (2001). Probability and Statistics.
Braun, W.J. and Murdoch, D.J (2007). A First course in Statistical Programming with R..
Statistical Inference (1990). G Casella & RL Berger.
Hannelore Liero and Silvelyn Zwanzig (2012). Introduction to the Theory of Statistical Inference.
Assessment
Summative
| Method | Percentage contribution |
|---|---|
| Coursework | 30% |
| Exam | 70% |
Referral
| Method | Percentage contribution |
|---|---|
| Exam ( hours) | 100% |
Costs
Costs associated with this module
Students are responsible for meeting the cost of essential textbooks, and of producing such essays, assignments, laboratory reports and dissertations as are required to fulfil the academic requirements for each programme of study.
In addition to this, students registered for this module typically also have to pay for:
Books and Stationery equipment
Course texts are provided by the library and there are no additional compulsory costs associated with the module.
Please also ensure you read the section on additional costs in the University’s Fees, Charges and Expenses Regulations in the University Calendar available at www.calendar.soton.ac.uk.