Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2008.10.009
Title: A simple approach for varying-coefficient model selection
Authors: Leng, C. 
Keywords: Component selection and smoothing operator
Smoothing spline
Varying-coefficient models
Issue Date: 1-Jul-2009
Citation: Leng, C. (2009-07-01). A simple approach for varying-coefficient model selection. Journal of Statistical Planning and Inference 139 (7) : 2138-2146. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2008.10.009
Abstract: In varying-coefficient models, an important question is to determine whether some of the varying coefficients are actually invariant coefficients. This article proposes a penalized likelihood method in the framework of the smoothing spline ANOVA models, with a penalty designed toward the goal of automatically distinguishing varying coefficients and those which are not varying. Unlike the stepwise procedure, the method simultaneously quantifies and estimates the coefficients. An efficient algorithm is given and ways of choosing the smoothing parameters are discussed. Simulation results and an analysis on the Boston housing data illustrate the usefulness of the method. The proposed approach is further extended to longitudinal data analysis. © 2008 Elsevier B.V. All rights reserved.
Source Title: Journal of Statistical Planning and Inference
URI: http://scholarbank.nus.edu.sg/handle/10635/104973
ISSN: 03783758
DOI: 10.1016/j.jspi.2008.10.009
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