Please use this identifier to cite or link to this item:
|Title:||A simple approach for varying-coefficient model selection||Authors:||Leng, C.||Keywords:||Component selection and smoothing operator
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.