Xinsheng Zhang
Fudan University
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Publication
Featured researches published by Xinsheng Zhang.
Computational Statistics & Data Analysis | 2017
Yong He; Xinsheng Zhang; Pingping Wang; Liwen Zhang
A multiple testing procedure is proposed to estimate the high dimensional Gaussian copula graphical model and nonparametric rank-based correlation coefficient estimators are exploited to construct the test statistics, which achieve modeling flexibility and estimation robustness. Compared to the existing methods depending on regularization technique, the proposed method avoids the ambiguous relationship between the regularized parameter and the number of false edges in graph estimation. It is proved that the proposed procedure can control the false discovery rate (FDR) asymptotically. Besides theoretical analysis, thorough numerical simulations are conducted to compare the graph estimation performance of the proposed method with some other state-of-the-art methods. The result shows that the proposed method works quite well under both non-Gaussian and Gaussian settings. The proposed method is then applied on a stock market data set to illustrate its empirical usefulness.
Computational Statistics & Data Analysis | 2018
Yong He; Xinsheng Zhang; Liwen Zhang
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.
Statistics & Probability Letters | 2013
Longxiang Fang; Xinsheng Zhang
Statistics & Probability Letters | 2010
Yunwen Ren; Xinsheng Zhang
Statistics & Probability Letters | 2015
Longxiang Fang; Xinsheng Zhang
Statistics & Probability Letters | 2011
Ying Ding; Xinsheng Zhang
Statistics & Probability Letters | 2009
Zhengyuan Wei; Xinsheng Zhang
Journal of Statistical Planning and Inference | 2013
Guangying Liu; Zhengyuan Wei; Xinsheng Zhang
Statistics & Probability Letters | 2004
Ying Ding; Xinsheng Zhang
Statistics & Probability Letters | 2011
Guangying Liu; Xinsheng Zhang