STATS 7014 - Statistics Topic B

North Terrace Campus - Semester 1 - 2015

Please contact the School of Mathematical Sciences for further details, or view course information on the School of Mathematical Sciences web site at

  • General Course Information
    Course Details
    Course Code STATS 7014
    Course Statistics Topic B
    Coordinating Unit Statistics
    Term Semester 1
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange Y
    Course Staff

    Course Coordinator: Associate Professor Robb Muirhead

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    In 2014, the topic of this course will be Statistical Decision Theory and Bayesian Applications.

    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.

    Learning Outcomes:

    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
  • Learning Resources
    Required Resources
    Recommended Resources
    T. 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 Learning
    All lectures, assignments, and solutions will be put on MyUni.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This 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 Summary
    Lecture 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

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Component Weighting Objective Assessed
    Exam 70% all
    Assignments 30% all
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    There 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.
    1. 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.
    Course Grading

    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.

  • Student Feedback

    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.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

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