Abstract
In this article, we study a model selection method for semiparametric transformation model. We consider the data is prevalent survival data which includes the situation with biased sampling. Semiparametric transformation model is a flexible model including the proportional hazard model and proportional odds model. The selection method's idea is from the random error of semiparametric transformation model. In order to do model selection, we introduce the pseudo-partial likelihood (Chen et al., 2009) to estimate coefficient and baseline function, and use these result to inference the random error by Wang(1991). We expect the estimation for random error's survival function should closed to it's setting. The smallest difference should be chosen as the model selection's result. Simulation shows that this idea can be confirmed by replication. We apply this method to a dementia data and a breast cancer data, form the selection's result we can give a contrary conclusion.