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Title
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Variable selection in varying-coefficient models using p-splines
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Author
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Abstract
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| In this article, we consider nonparametric smoothing and variable selection in varying-coefficient models. Varying-coefficient models are commonly used for analyzing the time-dependent effects of covariates on responses measured repeatedly (such as longitudinal data). We present the P-spline estimator in this context and show its estimation consistency for a diverging number of knots (or B-spline basis functions). The combination of P-splines with nonnegative garrote (which is a variable selection method) leads to good estimation and variable selection. Moreover, we consider APSO (additive P-spline selection operator), which combines a P-spline penalty with a regularization penalty, and show its estimation and variable selection consistency. The methods are illustrated with a simulation study and real-data examples. The proofs of the theoretical results as well as one of the real-data examples are provided in the online supplementary materials. |
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Language
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English
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Source (journal)
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Journal of computational and graphical statistics. - Alexandria, Va
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Publication
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Alexandria, Va
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2012
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ISSN
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1061-8600
[print]
1537-2715
[online]
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DOI
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10.1080/10618600.2012.680826
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Volume/pages
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21
:3
(2012)
, p. 638-661
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ISI
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000308282000005
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Full text (Publisher's DOI)
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