MA40092: Classical statistical inference
[Page last updated: 15 October 2020]
Academic Year:  2020/1 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 [equivalent to 12 CATS credits] 
Notional Study Hours:  120 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 

Assessment Summary:  EX 100% 
Assessment Detail: 

Supplementary Assessment: 

Requisites:  Before taking this module you must take MA20226 
Description:  Aims: To develop a formal basis for methods of statistical inference including criteria for the comparison of procedures. To give an in depth description of the asymptotic theory of maximum likelihood methods and hypothesis testing. Learning Outcomes: On completing the course, students should be able to: * calculate properties of estimates and tests; * derive efficient estimates and tests for a broad range of problems, including applications to a variety of standard distributions; * use the asymptotic theory for maximum likelihood estimators to derive approximate confidence intervals and tests. Skills: Numeracy T/F A Problem Solving T/F A Written and Spoken Communication F Content: Revision of standard distributions: Bernoulli, binomial, Poisson, exponential, gamma and normal, and their interrelationships. Sufficiency and Exponential families. Point estimation: Bias and variance considerations. RaoBlackwell theorem. CramerRao lower bound and efficiency. Unbiased minimum variance estimators and a direct appreciation of efficiency through some examples. Asymptotic theory for maximum likelihood estimators and its uses. Hypothesis testing: Review of the NeymanPearson lemma and maximisation of power. Compound alternative hypotheses, uniformly most powerful tests. Compound null hypotheses, monotone likelihood ratio property. Generalised likelihood ratio tests, asymptotic theory, nuisance parameters. Examples relevant to other final year statistics units. 
Programme availability: 
MA40092 is Optional on the following programmes:Department of Economics

Notes:
