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STATISTICAL INFERENCE (R)

Module code: ST862
Credits: 5
Semester: 2
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	Module Objective: To introduce students to Statistical Inference. Point estimation: method of moments and maximum likelihood. Consistency, bias and mean squared error. Information and the Cramer-Rao lower bound. Asymptotic distribution of MLEs. Confidence intervals. Testing hypotheses: power, Neymann Pearson, Likelihood ratio tests, large sample approximations. Bayesian estimation: prior and posterior distributions, loss functions, Bayes rule. Lectures and tutorials as for ST462.

Overview

Module Objective: To introduce students to Statistical Inference. Point estimation: method of moments and maximum likelihood. Consistency, bias and mean squared error. Information and the Cramer-Rao lower bound. Asymptotic distribution of MLEs. Confidence intervals. Testing hypotheses: power, Neymann Pearson, Likelihood ratio tests, large sample approximations. Bayesian estimation: prior and posterior distributions, loss functions, Bayes rule.

Lectures and tutorials as for ST462.

	Learning Outcomes
	On successful completion of the module, students should be able to: Use the method of moments and maximum likelihood to determine parameter estimators. Find the bias and mean square error of estimators. Derive approximate confidence intervals of maximum likelihood estimators based on their asymptotic distribution. State and prove the Rao-Blackwell Theorem. Find best unbiased estimators. Derive a likelihood ratio test for testing statistical hypotheses. Derive confidence intervals.

Learning Outcomes

Teaching & Learning methods

Assessment

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? No

	Pre-Requisites
	Registered Research student. Permission is required from the Head of the Mathematics & Statistics Department.

	Timetable
	This timetable is not currently available, please contact the Mathematics And Statistics Department.