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20112023
most citedA review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices

32 citations · 99 across the 26 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

math.PR2023

Exact Separation of Eigenvalues of Large Dimensional Noncentral Sample Covariance Matrices

Zhidong Bai, Jiang Hu, Jack W. Silverstein +1

Let $ \bbB_n =\frac{1}{n}(\bbR_n + \bbT^{1/2}_n \bbX_n)(\bbR_n + \bbT^{1/2}_n \bbX_n)^* $ where $ \bbX_n $ is a matrix with independent standardized random variables…

math.ST2023

KOO approach for scalable variable selection problem in large-dimensional regression

Zhidong Bai, Kwok Pui Choi, Yasunori Fujikoshi +1

An important issue in many multivariate regression problems is to eliminate candidate predictors with null predictor vectors. In large-dimensional (LD) setting where the numbers of…

stat.AP2023

Two-sample test of sparse stochastic block models

Qianyong Wu, Jiang Hu

The paper discusses a statistical problem related to testing for differences between two sparse networks with community structures. The community-wise edge probability matrices hav…

math.PR2023

No Eigenvalues Outside the Support of the Limiting Spectral Distribution of Large Dimensional noncentral Sample Covariance Matrices

Zhidong Bai, Jiang Hu, Jack W. Silverstein +1

Let $ \bbB_n =\frac{1}{n}(\bbR_n + \bbT^{1/2}_n \bbX_n)(\bbR_n + \bbT^{1/2}_n \bbX_n)^* $, where $ \bbX_n $ is a matrix with independent standardized random variable…

stat.ME2023

A spectral based goodness-of-fit test for stochastic block models

Qianyong Wu, Jiang Hu

Community detection is a fundamental problem in complex network data analysis. Though many methods have been proposed, most existing methods require the number of communities to be…