Semiparametric least squares support vector machine for accelerated failure time model

A lot of effort has been devoted to develop effective estimation methods for the accelerated failure time (AFT) model with censored data. The AFT model assumes a linear relationship between the logarithm of event time and covariates. In this paper we propose a semiparametric least squares support vector machine (LS-SVM) to consider situations where the functional form of the effect of one or more covariates is unknown. The proposed estimating equation can be easily computed by a simple linear equation system. We study the effect of several covariates on a censored response variable with an unknown probability distribution. We also provide a generalized cross-validation (GCV) method for choosing the hyperparameters which affect the performance of the proposed approach. The proposed method is evaluated through simulations and demonstrated using two real data examples.