Marra, G; Radice, R; (2010) Penalised regression splines: theory and application to medical research. Statistical Methods in Medical Research , 19 (2) 107 - 125. 10.1177/0962280208096688.
Full text not available from this repository.
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.
|Title:||Penalised regression splines: theory and application to medical research|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
Archive Staff Only: edit this record