Variable Selection for Partially Linear Varying Coefficient Transformation Models with Censored Data

In this paper, we study the problem of variable selection for varying coefficient transformation models with censored data. We fit the varying coefficient transformation models by maximizing the marginal likelihood subject to a shrinkage-type penalty, which encourages sparse solutions and hence facilitates the process of variable selection. We further provide an efficient computation algorithm to implement the proposed methods. A simulation study is conducted to evaluate the performance of the proposed methods and a real dataset is analyzed as an illustration.