TY - JOUR
T1 - Penalised regression splines
T2 - Theory and application to medical research
AU - Marra, Giampiero
AU - Radice, Rosalba
PY - 2010/4/1
Y1 - 2010/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77951679221&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1177/0962280208096688
U2 - 10.1177/0962280208096688
DO - 10.1177/0962280208096688
M3 - Article
SN - 0962-2802
VL - 19
SP - 107
EP - 125
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 2
ER -