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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Theory of Statistical Inference (MATH10028)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryNB. 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Several Variable Calculus and Differential Equations (MATH08063) AND Fundamentals of Pure Mathematics (MATH08064) AND ( Statistical Methodology (MATH10095) OR Likelihood (MATH10004))
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting 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 Inference2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate knowledge of the theory of statistical inference.
  2. Prove and apply results concerning statistical inference.
  3. Develop theoretical arguments.
  4. Demonstrate familiarity with dealing with multiparameter problems.
Reading List
None
Additional Information
Course URL https://info.maths.ed.ac.uk/teaching.html
Graduate Attributes and Skills Not entered
KeywordsTSI
Contacts
Course organiserDr Timothy Cannings
Tel:
Email: Timothy.Cannings@ed.ac.uk
Course secretaryMiss Sarah McDonald
Tel: (0131 6)50 5043
Email: sarah.a.mcdonald@ed.ac.uk
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