  The instability in the selection of models is a major concern with data sets
containing a large number of covariates. We focus on stability selection which
is used as a technique to improve variable selection performance for a range of
selection methods, based on aggregating the results of applying a selection
procedure to sub-samples of the data where the observations are subject to
right censoring. The accelerated failure time (AFT) models have proved useful
in many contexts including the heavy censoring (as for example in cancer
survival) and the high dimensionality (as for example in micro-array data). We
implement the stability selection approach using three variable selection
techniques--Lasso, ridge regression, and elastic net applied to censored data
using AFT models. We compare the performances of these regularized techniques
with and without stability selection approaches with simulation studies and a
breast cancer data analysis. The results suggest that stability selection gives
always stable scenario about the selection of variables and that as the
dimension of data increases the performance of methods with stability selection
also improves compared to methods without stability selection irrespective of
the collinearity between the covariates.
