Functional Data Analysis (Level M) STATS5056

  • Academic Session: 2019-20
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course introduces methods in functional data analysis, with an emphasis on practical issues and applications.

Timetable

15 lectures (1 or 2 each week)

5 2-hour computer-based practicals

Requirements of Entry

Some optional courses may be constrained by space and entry to these is not guaranteed unless you are in a programme for which this is a compulsory course.

Excluded Courses

STATS4073 Functional Data Analysis

Assessment

Assessment

Project work (25%) and final examination (75%)

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

To introduce the students to functional data analysis methods applied to a wide array of application areas;

To illustrate common numerical and estimation routines to perform functional data analysis;

To demonstrate applications where functional data analysis techniques have clear advantage over classical multivariate techniques.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ identify scenarios where data may be considered to be smooth functions and apply functional data analysis techniques;

■ construct visualization strategies and implement nonparametric smoothing for exploring functional data;

■ formulate and fit several types of functional linear models;

■ describe the functional principal component analysis algorithm and apply it in simple cases;

■ construct suitable methods for analysis involving derivatives and apply these techniques to provide solutions to practical problems;

■ discuss the principles behind registration and apply this technique to practical problems where registration is a crucial pre-processing step;

■ Understand one of the above topics in depth.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.