| Sign In to gain access to subscriptions and/or personal tools. |
Penalised regression splines: theory and application to medical research
Department of Mathematical Sciences, University of Bath, Bath, United Kingdom
* To whom correspondence should be addressed.
Generalised additive models (GAMs) allow for flexible functional dependence of a response variable on covariates. The aim of this article is to provide an accessible overview of GAMs based on the penalised likelihood approach with regression splines. In contrast to the classical backfitting, the penalised likelihood framework taken here provides researchers with an efficient computational method for automatic multiple smoothing parameter selection, which can determine the functional form of any relationship from the data. We illustrate through an example how the use of this methodology can help to gain insights into medical research.
First published on September 24, 2008 |
||||||
 |
|
|
 |
Sign In to gain access to subscriptions and/or personal tools.
