sta4ts theory of statistics
THEORY OF STATISTICS
STA4TS
2019
Credit points: 15
Subject outline
This subject builds on the knowledge of classical statistical inference developed in either STA3SI (Statistical Inference) or STA4SI (Statistical Inference). It consists of a selection of material from the following chapters of Casella and Berger (2002): Chapter 6 (Principles of Data Reduction), Chapter 7 (Point Estimation), Chapter 8 (Hypothesis Testing), Chapter 9 (Interval Estimation) and Chapter 10 (Asymptotic Evaluations). This also includes an introduction to the effect of model selection on confidence intervals.
SchoolSchool Engineering&Mathematical Sciences
Credit points15
Subject Co-ordinatorPaul Kabaila
Available to Study Abroad StudentsYes
Subject year levelYear Level 4 - UG/Hons/1st Yr PG
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites STA3SI or STA4SI or admission into SMDS.
Co-requisitesN/A
Incompatible subjects STA5TS
Equivalent subjectsN/A
Special conditions A sufficient background in probability and statistics is required to undertake this subject.
Learning resources
Readings
| Resource Type | Title | Resource Requirement | Author and Year | Publisher |
|---|---|---|---|---|
| Readings | Statistical Inference, 2nd edition (2002) | Recommended | Recommended text: Casella, G. and Berger, R.L. | . |
Graduate capabilities & intended learning outcomes
01. Present clear proofs of fundamental results in the advanced theory of statistical inference given in the lectures.
- Activities:
- Active participation in lectures. Preparation for the exam, which consists solely of proofs and sections of proofs from the lecture slides.
02. Derive mathematical calculations to investigate properties of data reduction by sufficiency, data reduction by ancillarity, data reduction by invariance, the assessment of confidence intervals and the effect of model selection on confidence intervals.
- Activities:
- Active participation in lectures. Also, solving assignment questions relating to these topics.
03. Write clear, well structured and rigorous proofs of results in the theory of statistical inference that the students have not seen in lectures. This includes appropriate use of statistical and mathematical vocabulary and notation.
- Activities:
- Assignments given out every week or second week involve theoretical derivations of results not stated in lectures. Each assignment consists of up to 100% assessed theoretical derivations.
04. Describe some important implications for statistical practice of the advanced theory of statistical inference.
- Activities:
- Active participation in lectures. Also, solving assignment questions relating to these implications.
Subject options
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Melbourne, 2019, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorPaul Kabaila
Class requirements
LectureWeek: 10 - 22
Two 1.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| Final examination (2-hour short answer) | 60 | 01, 02, 04 | |
| Seven assignments (approx. 400 words each) | 40 | 01, 02, 03, 04 |