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Variable selection in varying-coefficient models using p-splines
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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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English
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Journal of computational and graphical statistics. - Alexandria, Va | |
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Alexandria, Va : 2012
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1061-8600
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| Volume/pages |
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21:3(2012), p. 638-661
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000308282000005
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