Bi-level feature selection in high dimensional AFT models with applications to a genomic study

IF 0.9 4区数学 Q3 Mathematics

Statistical Applications in Genetics and Molecular Biology Pub Date : 2019-09-17 DOI:10.1515/sagmb-2019-0016

Hailin Huang, Jizi Shangguan, Peifeng Ruan, Hua Liang

引用次数: 2

Abstract

Abstract We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.

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高维AFT模型的双水平特征选择及其在基因组研究中的应用

摘要我们提出了一种新的高维加速失效时间模型的双层特征选择方法，将模型公式化为单指标模型。该方法在组和个体特征级别上产生稀疏解，并提供了一种计算高效且易于实现的权宜算法。我们分析了一个基因组数据集进行说明，并进行了模拟研究，以显示所提出方法的有限样本性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Applications in Genetics and Molecular Biology 生物-生化与分子生物学

CiteScore

1.20

自引率

11.10%

发文量

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.