| Unit name | Nonparametric Regression |
|---|---|
| Unit code | MATHM6004 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Kovac |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
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. In the most simple cases, we have enough information on the problem at hand to assume that the regression curve is known up to the value of some coefficients (for example, it is a straight line, but we need to estimate the coefficients of the line). Nonparametric methods are flexible techniques dedicated to treat more general cases: here, we construct a good estimator of the regression function without assuming that it has a specified shape. In this module, we will introduce popular nonparametric methods of regression estimation: local polynomial regression, regularisation techniques and wavelet thresholding. We will see how these methods can be applied in practice.
