Inference 3 STATS4012
- Academic Session: 2018-19
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
To present the fundamental principles of inference, with emphasis on likelihood and the large sample results that are widely used in practice.
Lectures: 2 hours per week (at times to be arranged)
Tutorials: fortnightly (at times to be arranged)
Requirements of Entry
The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.
Statistics 3I: Inference
Statistical Inference (Level M) [STATS5028]
The courses prescribed in the Honours or Master's programme to which the student has been admitted.
90-minute, end-of-course examination (100%)
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? Not applicable
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
To establish a solid understanding of the fundamental principles of likelihood-based inference, with emphasis on the large sample results that are widely used in practice.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ write down the likelihood, given a description of a model;
■ perform likelihood based inference for a variety of statistical models;
■ maximise likelihoods numerically;
■ calculate confidence intervals for model parameters;
■ test hypotheses about model parameters;
■ describe key theoretical properties that measure the effectiveness of procedures for point estimation, testing and interval estimation and the extent to which likelihood based methods possess such properties;
■ calculate power;
■ construct and maximise penalized likelihood function in simple cases;
■ discuss the principles behind the bootstrap and apply this technique to practical problems where information on the variability of estimates is required.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.