[KSCI] Korea Science Citation Index Service

REGRESSION WITH CENSORED DATA BY LEAST SQUARES SUPPORT VECTOR MACHINE

Kim, Dae-Hak (Department of Statistical Information, Catholic University of Daegu)
Shim, Joo-Yong (Department of Statistical Information, Catholic University of Daegu)
Oh, Kwang-Sik (Department of Statistical Information, Catholic University of Daegu)

Publication Information

Journal of the Korean Statistical Society / v.33, no.1, 2004 , pp. 25-34 More about this Journal

Abstract

In this paper we propose a prediction method on the regression model with randomly censored observations of the training data set. The least squares support vector machine regression is applied for the regression function prediction by incorporating the weights assessed upon each observation in the optimization problem. Numerical examples are given to show the performance of the proposed prediction method.

Keywords

Regression models; randomly censored data; least squares support vector machines;

Citations & Related Records

Reference

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2	Least square support vector machine classifier / [ Suykens,J.A.K.;Vandewalle,J. ] / Neural Processing Letters
3	M-estimation in censored linear models / [ Zhou,M. ] / Biometrika
4	Functions of positive and regative type, and their connection with the theory of integral equations / [ Mercer,J. ] / Philosophical Transactions of the Royal Society of London
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