Stability Selection for Lasso, Ridge and Elastic Net Implemented with AFT Models

TL;DR

This paper investigates the use of stability selection to enhance variable selection in high-dimensional censored data, applying Lasso, Ridge, and Elastic Net within AFT models, demonstrating improved stability and performance.

Contribution

It introduces a stability selection framework for AFT models with Lasso, Ridge, and Elastic Net, showing improved variable selection stability in high-dimensional censored data.

Findings

Stability selection improves variable selection stability.

Methods with stability selection perform better as data dimension increases.

Stability selection reduces variability in variable choice across samples.

Abstract

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.