STATS 7004 - Statistics Topic A

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 7004
    Course Statistics Topic A
    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: Professor Patricia Solomon

    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 Advanced Statistical Inference.

    Syllabus. This course will introduce students to the theory and practice of modern statistics. There will be four chapters covering the following topics. 1. Statistical inference: cumulants, the cumulant generating function, natural exponential family models, minimal sufficient statistics and completeness. Ancillary statistics, the profile log likelihood, marginal and conditional inference. 2. Model choice: principles of model selection, cross-validation (CV), Akaike's Information Criterion (AIC), Network Information Criterion (NIC), and the Kullback-Leibler distance. 3. Bootstrap methods of inference: non-parametric bootstrap estimates of an unknown sampling distribution, the bootstrap for assessing statistical accuracy, and bootstrap confidence intervals. 4. Survival analysis: censoring and truncation, failure time distributions, likelihood inference for parametric models, the non-parametric Kaplan-Meier estimator, the semi-parametric proportional hazards regression model (Cox model) and partial likelihood. Applications: Throughout the course, applications will be made to a range of subject areas using the statistical package R. Pre-requisites: It is assumed that students have attained at least a pass in Mathematical Statistics III (STATS 3006) and Statistical Modelling III (STATS 3001), or have equivalent knowledge.

    Course Learning Objectives

    Students who successfully complete the course should be able to:
    1. Demonstrate understanding of advanced principles of mathematical statistical inference.
    2. Demonstrate skills in modern statistical model selection methods using R.
    3. Demonstrate understanding of bootstrap inference and apply bootstrap methods of estimation.
    4. Understand the theory of survival analysis and analyse survival data.

    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
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. all
    Skills of a high order in interpersonal understanding, teamwork and communication. all
    A proficiency in the appropriate use of contemporary technologies. 2,3,4
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. all
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 2,3,4
  • Learning Resources
    Required Resources
    Recommended Resources
    Statistical models. A.C. Davison, CUP, 2008.
    Model selection and multimodel inference, second edition. K.P. Burnham and D. Anderson,  Springer, 2010.
    Bootstrap methods and their application. A.C. Davison and D. Hinkley, CUP, 1997.
    Analysis of survival data. D.R. Cox and D. Oakes, Chapman and Hall/CRC, 1984.
    Survival analysis: techniques for censored and truncated data. J.P. Klein and M.L. Moeschberger, Springer, 1997.
    Online Learning
    The lecture notes, assignments and other materials will be made available on the lecturer's webpage. Students should check their email regularly for any course notices or correspondence.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course will be delivered through 30 presented lectures over the semester. There will be four written assignments and a test in Week 7 to provide students with the opportunity to gauge their progress.

    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 4 40
    Test 1 26
    Total 156
    Learning Activities Summary
    Lecture Outline

    Modern statistical inference (lectures 1-10)
    Model choice (lectures 11-17)
    Bootstrap methods of inference (18-22)
    Survival analysis (23-30)
  • 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
    Activity Weighting Objective 
    Assignments 20% all
    Test 10% all
    Exam 70% all
    Assessment Related Requirements
    A final aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    Assessment Task Distributed Due Weighting
    Assignment 1 Week 1 Week 3 5%
    Assignment 2 Week 3 Week 6 5%
    Assignment 3 Week 6 Week 9 5%
    Assignment 4 Week 9 Week 12 5%
    Test Week 7 10%
    Final exam Examination period 70%
    1. All written assignments are to be submitted to the designated hand-in boxes within the School of Mathematical Sciences, or to the lecturer, with a signed cover sheet attached.
    2. Late assignments will not be accepted unless with by prior agreement with the lecturer. Please discuss delays owing to medical or compassionate reasons with the lecturer.
    3. Marked assignments will usually be returned to students within two weeks of submission.
    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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