Postgraduate Course: Incomplete Data Analysis (MATH11185)
|School||School of Mathematics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||This course is for MSc students who already have some undergraduate level background in statistics.
The course focuses on different techniques for dealing with missing data within a formal frequentist statistical framework.
Topics to be covered include :
- types of missingness;
- single and multiple imputation;
- mixture models;
- hidden Markov models; and
- the EM algorithm.
Course Delivery Information
|Academic year 2017/18, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 5%; Examination 95%
||Hours & Minutes
|Main Exam Diet S1 (December)|| Incomplete Data Analysis (MATH11185)||2:00|
On completion of this course, the student will be able to:
- understand different types of missingness.
- understand different statistical techniques for dealing with missing data and associated advantages and disadvantages.
- fit models to data with missing observations.
- interpret the output from statistical analyses.
|Statistical Analysis with Missing Data. Little and Rubin. Wiley.|
|Graduate Attributes and Skills
||These Postgraduate Taught courses may be taken by Undergraduate students *without* requiring a concession (NB. students on Postgraduate taught programmes are given priority in the allocation of places). For all other Postgraduate Taught courses the student and/or Personal Tutor must seek a concession.
|Course organiser||Dr Vanda Fernandes Inacio De Carvalho
Tel: (0131 6)50 4877
|Course secretary||Mrs Frances Reid
Tel: (0131 6)50 4883