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http://dx.doi.org/10.7465/jkdi.2012.23.2.393

Two-step LS-SVR for censored regression  

Bae, Jong-Sig (Department of Mathematics, Sungkyunkwan University)
Hwang, Chang-Ha (Department of Statistics, Dankook University)
Shim, Joo-Yong (Department of Data Science, Inje University)
Publication Information
Journal of the Korean Data and Information Science Society / v.23, no.2, 2012 , pp. 393-401 More about this Journal
Abstract
This paper deals with the estimations of the least squares support vector regression when the responses are subject to randomly right censoring. The estimation is performed via two steps - the ordinary least squares support vector regression and the least squares support vector regression with censored data. We use the empirical fact that the estimated regression functions subject to randomly right censoring are close to the true regression functions than the observed failure times subject to randomly right censoring. The hyper-parameters of model which affect the performance of the proposed procedure are selected by a generalized cross validation function. Experimental results are then presented which indicate the performance of the proposed procedure.
Keywords
Censored regression; generalized cross validation function; Kaplan-Meier estimator; kernel function; least squares support vector machine; randomly right censoring;
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Times Cited By KSCI : 4  (Citation Analysis)
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