STATS 7004 - Statistics Topic A
North Terrace Campus - Semester 1 - 2014
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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 Course Staff
Course Coordinator: Professor Patricia Solomon
Course Timetable
The full timetable of all activities for this course can be accessed from .
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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
None.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.Workload
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:
- 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.
Assessment Summary
Activity Weighting Objective
AssessedAssignments 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% Submission
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 .
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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 (http://www.adelaide.edu.au/policies/101/) 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.
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Student Support
- Academic Integrity for Students
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
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- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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