Regularized quantile regression under heterogeneous sparsity with application to quantitative genetic traits

被引:26
作者
He, Qianchuan [1 ]
Kong, Linglong [2 ]
Wang, Yanhua [3 ]
Wang, Sijian [4 ,5 ]
Chan, Timothy A. [6 ]
Holland, Eric [7 ]
机构
[1] Fred Hutchinson Canc Res Ctr, Div Publ Hlth Sci, Seattle, WA 98109 USA
[2] Univ Alberta, Dept Math & Stat Sci, Edmonton, AB T6G 2G1, Canada
[3] Beijing Inst Technol, Sch Math, Beijing 100081, Peoples R China
[4] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53706 USA
[5] Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
[6] Mem Sloan Kettering Canc Ctr, Human Oncol & Pathogenesis Program, New York, NY 10065 USA
[7] Fred Hutchinson Canc Res Ctr, Human Biol Div, Seattle, WA 98109 USA
基金
加拿大自然科学与工程研究理事会;
关键词
Heterogeneous sparsity; Quantitative traits; Variable selection; Quantile regression; Genomic features; VARIABLE SELECTION; INTERQUANTILE SHRINKAGE; ORACLE PROPERTIES; LASSO;
D O I
10.1016/j.csda.2015.10.007
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Genetic studies often involve quantitative traits. Identifying genetic features that influence quantitative traits can help to uncover the etiology of diseases. Quantile regression method considers the conditional quantiles of the response variable, and is able to characterize the underlying regression structure in a more comprehensive manner. On the other hand, genetic studies often involve high-dimensional genomic features, and the underlying regression structure may be heterogeneous in terms of both effect sizes and sparsity. To account for the potential genetic heterogeneity, including the heterogeneous sparsity, a regularized quantile regression method is introduced. The theoretical property of the proposed method is investigated, and its performance is examined through a series of simulation studies. A real dataset is analyzed to demonstrate the application of the proposed method. (c) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:222 / 239
页数:18
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