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 statistical framework.
Topics to be covered include:
- types of missingness;
- single imputation;
- likelihood based approaches for dealing with missing data (including the EM algorithm); and
- multiple imputation.
Entry Requirements (not applicable to Visiting Students)
|| Students MUST have passed:
Statistical Methodology (MATH10095)
||Other requirements|| None
Course Delivery Information
|Academic year 2023/24, Not available to visiting students (SS1)
|Course Start Date
|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)
||Individual written feedback.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- Demonstrate an understanding of the different types of missingness.
- Demonstrate an understand different statistical techniques for dealing with missing data and associated advantages and disadvantages.
- Demonstrate an ability to fit models to data with missing observations.
- Demonstrate an ability to interpret the output from statistical analyses.
- Demonstrate an ability to use the R statistical software to implement statistical procedures that can handle missing values.
|Statistical Analysis with Missing Data. Little and Rubin. Wiley. |
Applied Missing Data Analysis. Enders. Guilford Press.
Applied Multiple Imputation: Advantages, Pitfalls, New Developments, and Applications in R. Kleinke, Reinecke, Salfran and Spiess. Springer.
Flexible Imputation of Missing Data. Van Buuren. Chapman & Hall/CRC Press.
|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 Miguel Bras De Carvalho
Tel: (0131 6)50 4877
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268