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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17097

Title: Variable selection using P-splines
Authors: Gijbels, Irène
Verhasselt, Anneleen
Vrinssen, Inge
Issue Date: 2015
Citation: Wiley Interdisciplinary Reviews: Computational Statistics 7(1), p. 1-20.
Abstract: Selecting among a large set of variables those that influence most a response variable is an important problem in statistics.When the assumed regression model involves a nonparametric component, penalized regression techniques, and in particular P-splines, are among the commonly used methods. The aim of this paper is to provide a brief review of variable selection methods using P-splines. Starting frommultiple linear regression models,with least-squares regression, and Ridge regression, we review standard methods that perform variable selection, such as LASSO, nonnegative garrote, the SCAD method, etc. We briefly discuss a general framework of penalization and regularization methods. Going toward more flexible regression models, with some nonparametric component(s), we discuss P-splines estimation. For some examples of flexible regression models, we then review a few variable selection methods using P-splines. A brief discussion on grouped regularization techniques and on a robust variable selection method is given. Furthermore, we mention key ingredients in Bayesian approaches, and end the paper by drawing the attention to several other issues in variable selection with P-splines. Throughout the paper we provide some illustrations.
Notes: Correspondence to: irene.gijbels@wis.kuleuven.be
URI: http://hdl.handle.net/1942/17097
DOI: 10.1002/wics.1327
ISI #: 000367013700001
ISSN: 1939-0068
Category: A1
Type: Journal Contribution
Validation: vabb, 2016
Appears in Collections: Research publications

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