Postgraduate Course: Nonparametric Regression Models (MATH11186)
|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||A regression function is an important tool for describing the relation between two or more random variables. In real life problems, this function is usually unknown but can be estimated from a sample of observations. Nonparametric methods are flexible techniques dedicated to treat general cases where the shape of the regression curve is unknown.
In this course we will introduce nonparametric regression models and their application in practice using R.
Topics to be covered includes :
- general additive models;
- kernel estimation;
- wavelets; and
- the use of R for fitting nonparametric models.
Course Delivery Information
|Not being delivered|
On completion of this course, the student will be able to:
- Understand a range of methods for nonparametric regression and be able to apply them.
- Study asymptotic properties of nonparametric estimators.
- Use R to fit nonparametric regression models.
|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 Natalia Bochkina
Tel: 0131 650 8597
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268