In a wide spectrum of natural and social sciences, very often one encounters a large number of predictors for time to event data. An important task is to select right ones, and thereafter carry out the analysis. The
penalized regression, known as ”least absolute shrinkage and selection operator (LASSO)” has become a popular approach for predictor selection in last two decades. The LASSO regression involves a penalizing parameter (commonly denoted by
) that controls the extent of penalty, and hence plays a crucial role in identifying the right covariates. In this article, we propose an information theory-based method to determine the value of
under the accelerated failure time (AFT) model with extreme value distribution. Furthermore, an efficient algorithm is discussed in the same context. We demonstrate the usefulness of our method through an extensive simulation study.
