|Module Name||STATISTICAL INFERENCE II|
|Module Short Title||Classical inference|
|Contact Hours||3 hours per week, some of which will be tutorials|
|Module Personnel||Jason Wyse|
|Learning Outcomes||After taking this course the student will have a clear understanding
of the mechanisms underlying many hypothesis tests and confidence
intervals. The course will include a full treatment of estimation and
properties of estimators, as well as a light introduction to statistical
|Learning Aims||Understand the theory of distributions necessary to build tests/confidence intervals.
Learn how to construct confidence intervals based on pivots and large sample approximations.
Derive maximum likelihood and method of moments estimators for well known distributions.
Learn how to construct hypothesis tests for parameters.
Derive properties of estimators, including bias and mean squared error.
Understand asymptotic properties of maximum likelihood estimators.|
|Module Content||As in learning aims.|
|Recommended Reading List||There are many good introductory texts for mathematical statistics.
'Statistical Inference' by Berger and Casella contains much of the material relevant to this course.|
|Assessment Details||90% final exam and 10% assignments.|
|Academic Year of Data||2015/16|