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    Penalised regression splines: theory and application to medical research

    Marra, G. and Radice, Rosalba (2010) Penalised regression splines: theory and application to medical research. Statistical Methods In Medical Research 19 (2), pp. 107-125. ISSN 0962-2802.

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    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.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Rosalba Radice
    Date Deposited: 28 May 2013 09:35
    Last Modified: 28 May 2013 09:35
    URI: https://eprints.bbk.ac.uk/id/eprint/6798

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