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http://dx.doi.org/10.7465/jkdi.2013.24.1.201

Variable selection in censored kernel regression  

Choi, Kook-Lyeol (Department of Data Science, Inje University)
Shim, Jooyong (Department of Data Science, Inje University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.1, 2013 , pp. 201-209 More about this Journal
Abstract
For censored regression, it is often the case that some input variables are not important, while some input variables are more important than others. We propose a novel algorithm for selecting such important input variables for censored kernel regression, which is based on the penalized regression with the weighted quadratic loss function for the censored data, where the weight is computed from the empirical survival function of the censoring variable. We employ the weighted version of ANOVA decomposition kernels to choose optimal subset of important input variables. Experimental results are then presented which indicate the performance of the proposed variable selection method.
Keywords
ANOVA decomposition kernel; censored data; generalized cross validation function; kernel function; variable selection;
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Times Cited By KSCI : 4  (Citation Analysis)
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