Penalised regression splines: theory and application to medical research
Penalised regression splines: theory and application to medical research
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DOI：
10.1177/0962280208096688
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发表时间：
2010-04-01
影响因子：
2.3
通讯作者：
Radice, Rosalba
中科院分区：
文献类型：
--
作者：
Marra, Giampiero;Radice, Rosalba
广义加性模型（GAM）允许响应变量对协变量的灵活函数依赖。这篇文章的目的是提供一个可访问的GAM的基础上惩罚似然回归样条的方法概述。与经典的后拟合相反，这里采用的惩罚似然框架为研究人员提供了一种有效的计算方法，用于自动选择多个平滑参数，可以从数据中确定任何关系的函数形式。我们通过一个例子来说明如何使用这种方法可以帮助深入了解医学研究。
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.