Parametrization and penalties in spline models with an application to survival analysis
Costa, M. J. and Shaw, J. Ewart H.. (2009) Parametrization and penalties in spline models with an application to survival analysis. Computational Statistics & Data Analysis, Vol.53 (No.3). pp. 657-670. ISSN 0167-9473Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.csda.2008.07.026
A simple parametrization, built from the definition of cubic splines, is shown to facilitate the implementation and interpretation of penalized spline models, whatever configuration of knots is used. The parametrization is termed value-first derivative parametrization. Inference is Bayesian and explores the natural link between quadratic penalties and Gaussian priors. However, a full Bayesian analysis seems feasible only for some penalty functionals. Alternatives include empirical Bayes inference methods involving model selection type criteria. The proposed methodology is illustrated by an application to survival analysis where the usual Cox model is extended to allow for time-varying regression coefficients. (C) 2008 Elsevier B. V. All rights reserved.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QA Mathematics
|Divisions:||Faculty of Science > Statistics|
|Journal or Publication Title:||Computational Statistics & Data Analysis|
|Publisher:||Elsevier Science Ltd|
|Date:||15 January 2009|
|Number of Pages:||14|
|Page Range:||pp. 657-670|
|Access rights to Published version:||Restricted or Subscription Access|
|Funder:||Fundacao para a Ciencia e a Tecnologia|
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