Abstract
Single index model (SIMo) has become one of the most important semiparametric to overcome the dimensionality problem. This model work on summarizes the effects of the independent variables within a single variable and refers to them as the index. In this paper, the Bayesian hierarchical model is constructed to estimate the parameters and the unknown nonparametric function for the single index when the response variable is censored. In addition, to get a head start on finding the unknown nonparametric link function, we assume it follows the Gaussian process distribution. The Laplace distribution will be used as a prior distribution for the coefficient vector β when variables are being selected. The performance of the suggested technique is evaluated by applying it to simulation examples and actual data.
