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Semiparametric support vector machine for accelerated failure time model  

Hwang, Chang-Ha (Department of Statistics, Dankook University)
Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.4, 2010 , pp. 765-775 More about this Journal
Abstract
For the accelerated failure time (AFT) model a lot of effort has been devoted to develop effective estimation methods. AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric support vector machine to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be computed by a quadratic programming and a linear equation. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized approximate cross-validation method for choosing the hyper-parameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations using the artificial example.
Keywords
Accelerated failure time; generalized approximate cross validation function; hyper-parameters; semiparametric regression model; support vector machine;
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Times Cited By KSCI : 4  (Citation Analysis)
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