文章基本信息

标题：Sharp minimax tests for large covariance matrices and adaptation
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作者：Cristina Butucea ; Rania Zgheib
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2016
卷号：10
期号：2
页码：1927-1972
DOI：10.1214/16-EJS1143
语种：English
出版社：Institute of Mathematical Statistics
摘要：We consider the detection problem of correlations in a p-dimensional Gaussian vector, when we observe n independent, identically distributed random vectors, for n and p large. We assume that the covariance matrix varies in some ellipsoid with parameter a>1/2 and total energy bounded by L>0.
关键词：adaptive test;covariance matrix;goodness-of- fit tests;high-dimensional data;minimax separation rate;sharp asymptotic rate;U-statistic.