Undergraduate Course: Theory of Statistical Inference (MATH10028)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | NB. This course is delivered *biennially*. It is anticipated that it would then be delivered every other session.
Course for final year students in Honours programmes in Statistics.
Parametric families and likelihood. Sufficiency, Neyman factorisation, minimal sufficiency, joint sufficiency. Elements of statistical decision theory. Estimation, minimum variance unbiased estimators, Cramer-Rao lower bound, Bayes and minimax estimators. Hypothesis testing, pure significance tests, optimal tests, power, Neyman-Pearson lemma, uniformly most powerful tests. Confidence intervals, relationship to hypothesis testing. Selected topics in modern statistics. |
| Course description |
Not entered
|
Information for Visiting Students
| Pre-requisites | Visiting students are advised to check that they have studied the material covered in the syllabus of each pre-requisite course before enrolling. |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2019/20, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
|
| Assessment (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 5%, Examination 95% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Theory of Statistical Inference | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge of the theory of statistical inference.
- Prove and apply results concerning statistical inference.
- Develop theoretical arguments.
- Demonstrate familiarity with dealing with multiparameter problems.
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Contacts
| Course organiser | Dr Timothy Cannings
Tel:
Email: Timothy.Cannings@ed.ac.uk |
Course secretary | Miss Sarah McDonald
Tel: (0131 6)50 5043
Email: sarah.a.mcdonald@ed.ac.uk |
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