2016/04/25 by Md Hasinur Rahaman Khan, Khan, Md Hasinur Rahaman, Anamika Bhadra +3 · 1 citation
Mathematics · #Advanced Causal Inference Techniques #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.ME
paper · pdf · doi:10.48550/arxiv.1604.07311
24 pages, 6 figures, 2 tables. arXiv admin note: text overlap with arXiv:0809.2932 by other authors
arxiv created 2016/04/25 · openalex publication_date 2016/04/25 · arxiv updated 2016/04/26 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
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.