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Smoothing and Nonparametric Regression with Examples
Last Updated: 2026-02-05 15:23:46
Abstract
Methods of smoothing and nonparametric regression will be presented and illustrated via data examples from environmental and natural sciences. Principles as well as computational aspects will be discussed. Building of intuition will be emphasized. The main audience will be students in the Environmental Sciences and the Master of Statistics program.
Objective
The students will learn about methods of smoothing and nonparametric regression and application of concepts to data.
Content
Rough Outline: - Revision of basic material o probability distributions, random variables, expectations o basics of estimation and testing o basics of regression - Smoothing and nonparametric regression o Basic ideas, examples, models o Overview of smoothing methods o Kernel based methods o Selecting the smoothing parameter o Local polynomials o Correlated observations: time series and spatial data
Resources
Lecture Notes
Handouts will be made available periodically. However, lectures may contain additional information.
Literature
Suggested reading: Nonparametric Simple Regression, by John Fox, Sage Publications. Applied Nonparametric Regression, by Wolfgang Haerdle, Cambridge University Press. Applied Smoothing Techniques for Data Analysis: the Kernel Approach With S-Plus Illustrations, by Bowman, A.W., Azzalini, A., Oxford University Press. Kernel Smoothing, by M.P. Wand and M. C. Jones, Chapman and Hall.
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- oral 20 minutes
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Smoothing and Nonparametric Regression with Examples |
|
2 h weekly |