Nonparametric Regression (U04233)

? Credit Points : 10 ? SCQF Level : 10 ? Acronym : MAT-4-NPR

Course for final year students in Honours programmes in Mathematics.

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 methods such as kernel and spline smoothing, with emphasis on nonparametric wavelet regression. We will see how these methods can be applied in practice using Splus.

Entry Requirements

? Pre-requisites : Passes at C or better in MAT-2-FoC, MAT-2-SVC, MAT-2-LiA, MAT-2-MAM, MAT-2-Prb, MAT-2-SMe

Subject Areas

Home subject area

Specialist Mathematics & Statistics (Honours), (School of Mathematics, Schedule P)

Delivery Information

? Normal year taken : 4th year

? Delivery Period : Not being delivered

? Contact Teaching Time : 2 hour(s) per week for 11 weeks

All of the following classes

Type	Day	Start	End	Area
Lecture	Sunday	09:00	09:50	KB

Summary of Intended Learning Outcomes

1. Knowledge of methods for nonparametric regression and ability to apply them.
2. Familiarity with the Bayesian approach in wavelet nonparametric regression.
3. Ability to use Splus to fit a nonparametric regression model.