Semiparametric support vector machine for accelerated failure time model
Changha Hwang, Jooyong Shim · 2010
Abstract For the accelerated failure time (AFT) model a lot of e ort has been devoted to de-velop e ective estimation methods. AFT model assumes a linear relationship betweenthe logarithm of event time and covariates. In this paper we propose a semiparamet-ric support vector machine to consider situations where the functional form of thee ect of one or more covariates is unknown. The proposed estimating equation canbe computed by a quadratic programming and a linear equation. We study the ef-fect of several covariates on a censored response variable with an unknown probabilitydistribution. We also provide a generalized approximate cross-validation method forchoosing the hyper-parameters which a ect the performance of the proposed approach.The proposed method is evaluated through simulations using the arti cial example.Keywords: Accelerated failure time, generalized approximate cross validation function,hyper-parameters, semiparametric regression model, support vector machine. 1. Introduction