Abstract
Regression models are extremely important in describing relationships between variables. Linear regression is a simple, but powerful tool in investigating linear dependencies. It relies, however, on strict distributional assumptions. Nonparametric regression models are widely used, because fewer assumptions about the data at hand are necessary.
Keywords
- Strict Distributional Assumptions
- Nonparametric Regression
- Fixed Design Problem
- Cross-validation Algorithm
- Kernel Regression
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Everything must be made as simple as possible. But not simpler.
— Albert Einstein
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Härdle, W.K., Okhrin, O., Okhrin, Y. (2017). Regression Models. In: Basic Elements of Computational Statistics. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-55336-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-55336-8_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55335-1
Online ISBN: 978-3-319-55336-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)