MATH3044 Statistical Inference
Statistical inference involves using data from a sample to draw conclusions about a wider population. Given a partly specified statistical model, in which at least one parameter is unknown, and some observations for which the model is valid, it is possible to draw inferences about the unknown parameters and hence about the population from which the sample is drawn. As such, inference underpins all aspects of statistics. However, inference can take different forms. It may be adequate to provide a point estimate of a parameter, i.e. a single number. More usually, an interval is required, giving a measure of precision. It may also be necessary to test a pre-specified hypothesis about the parameter(s). These forms of inference can all be considered as special cases of the use of a decision function. There are a number of different philosophies about how these inferences should be drawn, ranging from that which says the sample contains all the information available about a parameter (likelihood), through that which says account should be taken of what would happen in repeated sampling (frequentist), to that which allows the sample to modify prior beliefs about a parameter’s value (Bayesian). This Module aims to explore these approaches to parametric statistical inference, particularly through application of the methods to numerous examples.
Aims and Objectives
Derive suitable point estimators of the parameters of the distribution of a random variable and give a measure of their precision
Having successfully completed this module you will be able to:
- Develop the abiiity to explain mathematical results in a language understandable by biologists
- Understand and apply the concept of stability of a fixed point solution of a system of ordinary and partial differential equations
- Solve mathematically and interpret biologically simple problems involving one- and two-species ecosystems and biochemical reactions
- Understand and apply the basis of pattern formation in PDE-based models of biological systems
Point Estimation Sufficiency and the factorisation theorem Maximum Likelihood Estimation (MLE) Unbiased estimation Sufficiency Rao-Blackwell theorem Cramer-Rao lower bound Minimum Variance Unbiased Estimators (MVUE) Asymptotic efficiency Hypothesis Testing Neyman-Pearson Lemma Uniformly most powerful test Likelihood ratio test Computational Inference Numerical solutions to Maximum Likelihood Estimation – Newton-Raphson and Fisher Scoring Re-sampling methods – Jacknife and Bootstrap Bayesian Inference Bayes theorem Prior and posterior distributions Uniform and conjugate prior distributions Predictive inference Decision-Based Inference Loss functions and risk functions Minimax decisions Admissibility Bayes risk
Learning and Teaching
Teaching and learning methods
Lectures, coursework, exercises, private study
|Total study time||150|
Resources & Reading list
Lee PM (2004). Bayesian Statistics : An Introduction.
Garthwaite OH, Jolliffe IT & Jones B (2002). Statistical Inference.
Mukhopadhyay H (2006). Introductory Statistical Inference.
Severini TA (2000). Likelihood Methods in Statistics.
Young GA & Smith RL (2005). Essentials of Statistical Inference.
80% written exam, 20% coursework Referral arrangements: Written Examination
To study this module, you will need to have studied the following module(s):
|MATH2011||Statistical Distribution Theory|
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.