Publisher : Korean Data and Information Science Society
DOI : 10.7465/jkdi.2016.27.4.1059
Title & Authors
Robust varying coefficient model using L1 regularization Hwang, Changha; Bae, Jongsik; Shim, Jooyong;
In this paper we propose a robust version of varying coefficient models, which is based on the regularized regression with L1 regularization. We use the iteratively reweighted least squares procedure to solve L1 regularized objective function of varying coefficient model in locally weighted regression form. It provides the efficient computation of coefficient function estimates and the variable selection for given value of smoothing variable. We present the generalized cross validation function and Akaike information type criterion for the model selection. Applications of the proposed model are illustrated through the artificial examples and the real example of predicting the effect of the input variables and the smoothing variable on the output.
Akaike's information criterion;generalized cross validation function;iteratively reweighted least squares procedure;L1-regularization;locally weighted regression;smoothing variable;variable selection;varying coefficient model;
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