Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering-Reference-Cited by-同舟云学术

Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering

Published:2016-07-05 Issue:1 Volume:73 Page:31-41
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biom

Author:

Chang Jinyuan¹²,Zhou Wen³,Zhou Wen-Xin⁴,Wang Lan⁵

Affiliation:

1. School of Statistics; Southwestern University of Finance and Economics; Chengdu Sichuan 611130 China

2. School of Mathematics and Statistics; University of Melbourne; Parkville Victoria 3010 Australia

3. Department of Statistics; Colorado State University; Fort Collins Colorado 80523 U.S.A.

4. Department of Operations Research and Financial Engineering; Princeton University; Princeton New Jersey 08544 U.S.A.

5. School of Statistics; University of Minnesota; Minneapolis Minnesota 55455 U.S.A.

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/biom.12552/fullpdf

Reference24 articles.

1. The control of the false discovery rate in multiple testing under dependency;Benjamini;The Annals of Statistics,2001

2. Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings;Cai;Journal of the American Statistical Association,2013

3. Asymptotic properties of computationally efficient alternative estimators for a class of multivariate normal models;Caragea;Journal of Multivariate Analysis,2007

4. Chang , J. Zhou , W. Zhou , W.-X. 2014 Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity

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