Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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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)
  • Received : 2010.05.07
  • Accepted : 2010.07.05
  • Published : 2010.07.31
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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

Acknowledgement

Supported by : National Research Foundation of Korea (NRF)

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