STATS 7014 - Statistics Topic B
North Terrace Campus - Semester 1 - 2016
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General Course Information
Course Details
Course Code STATS 7014 Course Statistics Topic B Coordinating Unit Mathematical Sciences Term Semester 1 Level Postgraduate Coursework Location/s North Terrace Campus Units 3 Available for Study Abroad and Exchange Y Assessment Ongoing assessment 30%, exam 70% 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 2016, the topic of this course is Advanced Statistical Inference.
Syllabus:
This course is about modern statistical theory and practice. 1. Statistical inference: cumulants, the cumulant generating function, natural exponential family models, minimal sufficient statistics, completeness, the multi-parameter profile likelihood. 2. Model choice: principles of model selection, cross-validation (CV), model misspecification and the Kullback-Leibler criterion, Akaike's Information Criterion (AIC). 3. Bootstrap methods: the non-parametric bootstrap for estimating sampling distributions, other types of bootstrap, bootstrap confidence intervals. 4. Missing data: types of missingness, publication bias, the Expectation-Maximisation (EM) algorithm. 5. Generalised linear models: density and link functions, estimation and inference, the analysis of deviance. Applications: throughout the course, applications will be made to a range of subject areas using the statistical package R.
Pre-requisites: Mathematical Statistics III (STATS 3006) and Statistical Modelling III (STATS 3001), or equivalent knowledge.
Learning Outcomes:
On successful completion of this course, students should be able to:
1. demonstrate their understanding of advanced principles of mathematical statistical inference;
2. understand the principles of model selection and conduct model selection methods such as cross-valudation using R;
3. demonstrate their understanding of bootstrap inference and apply bootstrap methods of estimation in practice;
4. recognise the different types of missing data, conduct inference for missing data and apply the EM algorithm; and
5. demonstrate understanding of the theory generalised linear models and fit GLMs to 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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
All Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
All Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
All Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
2,3,4,5 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
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Learning Resources
Required Resources
There are no required resources for this course.Recommended Resources
The main reference text for the course is the following book by Anthony Davison. The other texts provide more detail on each topic and all these books are in the Barr Smith library:
1. Statistical models. A.C. Davison. CUP, 2008.
2. The elements of statistical learning, Second edition. T. Hastie, R. Tibshirani and J. Friedman, Springer, 2009.
3. Model selection and multimodel inference, Second edition. K.P. Burnham and D. Anderson, Springer, 2010.
4. Bootstrap methods and their application. A.C. Davison and D. Hinkley, CUP, 1997.
5. Generalized linear models, Second edition. P. McCullagh and J. Nelder, Chapman and Hall/CRC, 1989.
6. Modern applied statistics with S, Fourth edition. W. Venables and B. Ripley, Springer. 2002.
Online Learning
The course will have an active MyUni website. -
Learning & Teaching Activities
Learning & Teaching Modes
The lecturer guides the students through the course material in 30 lectures. Students are expected to engage with the material in the lectures. Interaction with the lecturer and discussion of any difficulties that arise during the lecture is encouraged. Fortnightly assignments help students strengthen their understanding of the theory and practical work, and to help them gauge their progress.
Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
ActivityQuantity Workload hours Lectures 30 90 Assignments 5 50 Test 1 16 Total 156 Learning Activities Summary
Lecture Outline
1. Modern statistical inference (lectures 1-8)
2. Model choice (lectures 9-15)
3. Bootstrap methods (lecture 16-20)
4. Missing data (lectures 21-25)
5. Generalised linear models (lectures 26-30)
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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
Assessment task Task type Due Weighting Objective Assessed Assignments Formative and summative Weeks
3,5,7,8,1220% all Test Summative Midsemester 10% 1,2 Examination Summative Examination
period70% all Assessment Related Requirements
An 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 4% Assignment 2 Week 3 Week 5 4% Assignment 3 Week 5 Week 7 4% Assignment 4 Week 7 Week 9 4% Assignment 5 Week 9 Week 11 4% Test Midsemester 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
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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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
- Copyright Compliance Policy
- 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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