THEORY OF STATISTICS

STA4TS

2020

Credit points: 15

Subject outline

This subject builds on the knowledge of classical statistical inference developed in STM3SI (Statistical Inference). You will be introduced to proofs of some fundamental results in the advanced theory of statistical inference, including principles of data reduction, point estimation, hypothesis testing, interval estimation, asymptotic evaluations and the effect of model selection on confidence intervals. Some of these proofs are presented by the teacher in lectures and some of these proofs are derived independently by the you as parts of your assignments. In addition, the subject introduces you to some important practical applications of advanced theory of statistical inference. This is achieved both by the teacher describing these applications in lectures and you discovering these applications through carrying out your assignments.

School: Engineering and Mathematical Sciences (Pre 2022)

Credit points: 15

Subject Co-ordinator: Paul Kabaila

Available to Study Abroad/Exchange Students: Yes

Subject year level: Year Level 4 - UG/Hons/1st Yr PG

Exchange Students: No

Available as Elective: No

Learning Activities: N/A

Capstone subject: No

Subject particulars

Subject rules

Prerequisites: STA3SI or STM3SI or STA4SI or admission into SMDS

Co-requisites: N/A

Incompatible subjects: STA5TS

Equivalent subjects: N/A

Quota Management Strategy: N/A

Quota-conditions or rules: N/A

Special conditions: A sufficient background in probability and statistics is required to undertake this subject.

Minimum credit point requirement: N/A

Assumed knowledge: N/A

Learning resources

Statistical Inference

Resource Type: Book

Resource Requirement: Recommended

Author: Recommended text: Casella, G. and Berger, R.L .

Year: 2002

Edition/Volume: 2nd edition

Publisher: Duxbury

ISBN: N/A

Chapter/article title: N/A

Chapter/issue: N/A

URL: N/A

Other description: N/A

Source location: N/A

Career Ready

Career-focused: No

Work-based learning: No

Self sourced or Uni sourced: N/A

Entire subject or partial subject: N/A

Total hours/days required: N/A

Location of WBL activity (region): N/A

WBL addtional requirements: N/A

Graduate capabilities & intended learning outcomes

Graduate Capabilities

Intended Learning Outcomes

01. Justify the steps in the proofs of the results in the theory of statistical inference given in the lectures.

02. Derive mathematical calculations to investigate properties of data reduction by sufficiency, data reduction by ancillary, data reduction by invariance, the assessment of confidence intervals and the effect of model selection on confidence intervals.

03. Write clear, well structured and rigorous proofs of results in the theory of statistical inference that you have not seen in lectures.

04. Critically review the implications for statistical practice of the advanced theory of statistical inference.

Melbourne (Bundoora), 2020, Semester 1, Day

Overview

Online enrolment: Yes

Maximum enrolment size: N/A

Enrolment information: N/A

Subject Instance Co-ordinator: Paul Kabaila

Class requirements

LectureWeek: 10 - 22
Two 1.00 hour lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.

Assessments

Assessment element	Comments	Category	Contribution	Hurdle	%	ILO*
2-hour short answer final examination (2,000 word equivalent)Only the material covered in the lectures is examinable	N/A	N/A	N/A	No	60	SILO1, SILO2, SILO4
Five assignments (2,500 word equivalent total))Approx. 500 words each.Students are provided with the marking scheme used for the assignments	N/A	N/A	N/A	No	40	SILO1, SILO2, SILO3, SILO4