Adaptive LASSO for general transformation models with right censored data
In this paper, we consider variable selection for general transformation models with right censored data and propose a unified procedure for both variable selection and estimation. We conduct the proposed procedure by maximizing penalized log-marginal likelihood function with Adaptive LASSO penalty (ALASSO) on regression coefficients. Two main advantages of this procedure are as follows: (i) the penalties can be assigned to regression coefficients adaptively by data according to the importance of corresponding covariates; (ii) it is free of baseline survival function and censoring distribution. Under some regular conditions, we show that the penalized estimates with ALASSO are root n-consistent and enjoy oracle properties. Some simulation examples and Primary Biliary Cirrhosis Data application illustrate that our proposed procedure works very well for moderate sample size. (C) 2012 Elsevier B.V. All rights reserved.
lasso general transformation models penalized log-marginal likelihood alasso oracle scad consistency