Penalised regression splines: Theory and application to medical research

Giampiero Marra, Rosalba Radice

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)107-125
Number of pages19
JournalStatistical Methods in Medical Research
Volume19
Issue number2
DOIs
Publication statusPublished - 1 Apr 2010

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