A nonparametric empirical Bayes approach to large-scale multivariate regression

被引:4
作者
Wang, Yihe [1 ]
Zhao, Sihai Dave [1 ]
机构
[1] Univ Illinois, Dept Stat, Urbana, IL 61820 USA
关键词
Compound decision; Multivariate regression; Nonparametric; Empirical Bayes; CONVEX-OPTIMIZATION; MAXIMUM-LIKELIHOOD; LINEAR-REGRESSION; MODELS;
D O I
10.1016/j.csda.2020.107130
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Multivariate regression has many applications, ranging from time series prediction to genomics. Borrowing information across the outcomes can improve prediction error, even when outcomes are statistically independent. Many methods exist to implement this strategy, for example the multiresponse lasso, but choosing the optimal method for a given dataset is difficult. These issues are addressed by establishing a connection between multivariate linear regression and compound decision problems. A nonparametric empirical Bayes procedure that can learn the optimal regression method from the data itself is proposed. Furthermore, the proposed procedure is free of tuning parameters and performs well in simulations and in a multiple stock price prediction problem. (C) 2020 Elsevier B.V. All rights reserved.
引用
收藏
页数:13
相关论文
共 36 条
[11]   A gene-based association method for mapping traits using reference transcriptome data [J].
Gamazon, Eric R. ;
Wheeler, Heather E. ;
Shah, Kaanan P. ;
Mozaffari, Sahar V. ;
Aquino-Michaels, Keston ;
Carroll, Robert J. ;
Eyler, Anne E. ;
Denny, Joshua C. ;
Nicolae, Dan L. ;
Cox, Nancy J. ;
Im, Hae Kyung .
NATURE GENETICS, 2015, 47 (09) :1091-+
[12]  
Gruber Marvin H. J., 1998, STAT TEXTB MONOG, V156
[13]   Integrative approaches for large-scale transcriptome-wide association studies [J].
Gusev, Alexander ;
Ko, Arthur ;
Shi, Huwenbo ;
Bhatia, Gaurav ;
Chung, Wonil ;
Penninx, Brenda W. J. H. ;
Jansen, Rick ;
de Geus, Eco J. C. ;
Boomsma, Dorret I. ;
Wright, Fred A. ;
Sullivan, Patrick F. ;
Nikkola, Elina ;
Alvarez, Marcus ;
Civelek, Mete ;
Lusis, Aldons J. ;
Lehtimaki, Terho ;
Raitoharju, Emma ;
Kahonen, Mika ;
Seppala, Ilkka ;
Raitakari, Olli T. ;
Kuusisto, Johanna ;
Laakso, Markku ;
Price, Alkes L. ;
Pajukanta, Paivi ;
Pasaniuc, Bogdan .
NATURE GENETICS, 2016, 48 (03) :245-252
[14]   Imaging genetics: Perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing [J].
Hariri, Ahmad R. ;
Drabant, Emily M. ;
Weinberger, Daniel R. .
BIOLOGICAL PSYCHIATRY, 2006, 59 (10) :888-897
[15]  
James W., 1961, P 4 BERK S PROB STAT, VI, P367
[16]   GENERAL MAXIMUM LIKELIHOOD EMPIRICAL BAYES ESTIMATION OF NORMAL MEANS [J].
Jiang, Wenhua ;
Zhang, Cun-Hui .
ANNALS OF STATISTICS, 2009, 37 (04) :1647-1684
[17]  
JOHNSTONE I. M., 2017, GAUSSIAN ESTIMATION
[18]   The three-pass regression filter: A new approach to forecasting using many predictors [J].
Kelly, Bryan ;
Pruitt, Seth .
JOURNAL OF ECONOMETRICS, 2015, 186 (02) :294-316
[19]   Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules [J].
Koenker, Roger ;
Mizera, Ivan .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2014, 109 (506) :674-685
[20]  
Lashkari D., 2008, ADV NEURAL INFORM PR, P825