Paper Info
Title
Variable selection for high dimensional Gaussian copula regression model: An adaptive hypothesis testing procedure.
Abstract
In this paper we consider the variable selection problem for high dimensional Gaussian copula regression model. We transform the variable selection problem into a multiple testing problem. Compared to the existing methods depending on regularization or a stepwise algorithm, our method avoids the ambiguous relationship between the regularized parameter and the number of false discovered variables or the decision of a stopping rule. We exploit nonparametric rank-based correlation coefficient estimators to construct our test statistics which achieve robustness and adaptivity to the unknown monotone marginal transformations. We show that our multiple testing procedure can control the false discovery rate (FDR) or the average number of falsely discovered variables (FDV) asymptotically. We also propose a screening multiple testing procedure to deal with the extremely high dimensional setting. Besides theoretical analysis, we also conduct numerical simulations to compare the variable selection performance of our method with some state-of-the-art methods. The proposed method is also applied on a communities and crime unnormalized data set to illustrate its empirical usefulness.
Year
DOI
Venue
2018
10.1016/j.csda.2018.03.003
Computational Statistics & Data Analysis
Keywords
Field
DocType
Gaussian copula regression,Variable selection,Multiple testing,FDR/FDV
False discovery rate,Feature selection,Regression analysis,Copula (probability theory),Algorithm,Multiple comparisons problem,Nonparametric statistics,Statistics,Mathematics,Statistical hypothesis testing,Estimator
Journal
Volume
ISSN
Citations 
124
0167-9473
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Yong He17812.64
Xinsheng Zhang252.32
Liwen Zhang363.55