STATS 7014 - Statistics Topic B
North Terrace Campus - Semester 1 - 2014
General Course Information
Course Code STATS 7014 Course Statistics Topic B Coordinating Unit Statistics Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Course Description Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at http://www.maths.adelaide.edu.au/students/honours
Course Coordinator: Associate Professor Robb Muirhead
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning OutcomesIn 2014, the topic of this course will be Statistical Decision Theory and Bayesian Applications.
Syllabus: This course will provide an introduction to many of the facets of modern statistical decision theory. It will focus on the following topics: connection between game theory and decision theory; utility and loss functions; decision functions and risk functions; randomised decisions; basic Bayes theory; optimality of decision rules; Bayes decision rules; admissibility and complete classes; the minimax theorem and the complete class theorem; connection between sufficiency and complete classes; invariant decision rules; hypothesis testing. It is assumed that students will have a statistical background equivalent to Mathematical Statistics III.
Students who successfuly complete the course should:
1. understand the role played by statistical decision theory in the area of statistical inference
2. be able to construct optimal decision rules in a variety of settings
3. appreciate the implications and uses of Bayesian methodology
University Graduate Attributes
This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:
University Graduate Attribute Course Learning Outcome(s) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. all The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. all An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. all A proficiency in the appropriate use of contemporary technologies. all A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. all
Recommended ResourcesT. S. Ferguson, Mathematical Statistics: A Decision Theoretic Approach, Academic Press
M. H. DeGroot, Optimal Statistical Decisions, Wiley
J. O. Berger, Statistical Decision Theory and Bayesian Analysis, Springer-Verlag
Online LearningAll lectures, assignments, and solutions will be put on MyUni.
Learning & Teaching Activities
Learning & Teaching ModesThis course relies on lectures as the primary delivery mechanism for the material. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding.
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 30 90 Assignments 5 66 Total 156
Learning Activities SummaryLecture Outline
1, 2: Basic elements of a game
3, 4: Relation between game theory and statistical decision theory
5: Utility and loss functions
6, 7: Decision functions and risk functions
8, 9: Randomised actions and decisions
10-12: Basic Bayes methodology
13, 14: Optimality of decision rules
15, 16: Bayes decision rules
17, 18: Complete classes of decision rules
19-21: Admissible rules and admissibility of Bayes rules
22: The minimax theorem
23: The complete class theorem
24: Sufficient statistics and complete classes
25-28: Elements of invariance, invariant decision rules
29, 30: Hypothesis testing, locally best tests
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Component Weighting Objective Assessed Exam 70% all Assignments 30% all
Assessment Related RequirementsAn aggregate score of at least 50% is required to pass the course.
Assessment DetailThere will be five equally weighted assignments (6% each), due on Thursday in weeks 3, 5, 7, 9, 11. These will be distributed to students on Monday in weeks 2, 4, 6, 8, 10.
Submission1. All written assignments are to be given to the lecturer or left in the box outside the lecturer's office by the designated due time.
2. Late assignments will not be accepted.
3. Assignments will have a two week turn-around time for feedback to students.
Grades for your performance in this course will be awarded in accordance with the following scheme:
M10 (Coursework Mark Scheme) Grade Mark Description FNS Fail No Submission F 1-49 Fail P 50-64 Pass C 65-74 Credit D 75-84 Distinction HD 85-100 High Distinction CN Continuing NFE No Formal Examination RP Result Pending
Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through Access Adelaide.
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
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