MT4606 Classical Statistical Inference

Academic year

2025 to 2026 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students

Planned timetable

10.00 am Mon (weeks 1, 3, 5, 7, 9, 12), Wed and Fri

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Dr B R Baer

Dr B R Baer
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module Staff

Dr Ben Baer

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module aims to show how the methods of estimation and hypothesis testing met in 2000- and 3000-level Statistics modules can be justified and derived; to extend those methods to a wider variety of situations. The syllabus includes: sufficiency, comparison of point estimators; the Rao-Blackwell Theorem; minimum variance unbiased estimators; Fisher information and the Cramer-Rao lower bound; maximum likelihood estimation; theory of Generalized Linear Models; hypothesis-testing; confidence sets.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT3507

Anti-requisites

YOU CANNOT TAKE THIS MODULE IF YOU TAKE MT5701

Assessment pattern

2-hour Written Examination = 100%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (weeks 1 - 10) and 0.5 tutorial (weeks 2 - 11).

Scheduled learning hours

30

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

120

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Explain key ideas and notions of statistics such as bias, (minimal) sufficiency, efficiency, consistency, and uniformly most powerful test
  • Apply important theorems of classical statistics to derive unbiased parameter estimators that attain minimum variance, and hypothesis tests that are uniformly most powerful
  • Use likelihood methods for finding parameter estimators, for computing the available information about parameters, and for deriving hypothesis tests; and describe the properties of these methods
  • Identify probability distributions that belong to the exponential family and derive statistics that are minimally sufficient, complete, and efficient, as well as uniformly most powerful tests, and generalised linear models for data that follow these probability distributions