Stability selection for lasso, ridge and elastic net implemented with AFT models

IF 0.9 4区 数学 Q3 Mathematics
M. H. R. Khan, Anamika Bhadra, Tamanna Howlader
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引用次数: 19

Abstract

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 two real data examples–a breast cancer data and a diffuse large B-cell lymphoma data. 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.
利用AFT模型实现套索、脊网和弹性网的稳定性选择
对于包含大量协变量的数据集,模型选择中的不稳定性是一个主要问题。我们专注于稳定性选择,这是一种用于改善一系列选择方法的变量选择性能的技术,基于将选择程序应用于观测数据的子样本的结果聚合在一起,其中观察结果受到正确的审查。加速失效时间(AFT)模型已被证明在许多情况下是有用的,包括重审查(例如在癌症生存中)和高维(例如在微阵列数据中)。我们使用三种变量选择技术——lasso、脊回归和弹性网来实现稳定性选择方法,这些技术应用于使用AFT模型的截尾数据。我们通过模拟研究和两个真实数据示例(乳腺癌数据和弥漫性大b细胞淋巴瘤数据)比较了这些正则化技术在使用和不使用稳定性选择方法时的性能。结果表明,稳定性选择给出了始终稳定的变量选择场景,并且随着数据维数的增加,无论协变量之间是否共线性,具有稳定性选择的方法的性能也比不具有稳定性选择的方法有所提高。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
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