A simultaneous test of mean vector and covariance matrix in high-dimensional settings

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2020.09.003 ◽

2021 ◽

Vol 212 ◽

pp. 141-152

Author(s):

Mingxiang Cao ◽

Peng Sun ◽

Junyong Park

Keyword(s):

Covariance Matrix ◽

High Dimensional ◽

Simultaneous Test ◽

Mean Vector

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On LR simultaneous test of high-dimensional mean vector and covariance matrix under non-normality

Statistics & Probability Letters ◽

10.1016/j.spl.2018.10.008 ◽

2019 ◽

Vol 145 ◽

pp. 338-344 ◽

Cited By ~ 1

Author(s):

Zhenzhen Niu ◽

Jiang Hu ◽

Zhidong Bai ◽

Wei Gao

Keyword(s):

Covariance Matrix ◽

High Dimensional ◽

Simultaneous Test ◽

Mean Vector

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Simultaneous testing of the mean vector and covariance matrix among k populations for high-dimensional data

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2019.1639751 ◽

2019 ◽

pp. 1-22 ◽

Cited By ~ 1

Author(s):

Masashi Hyodo ◽

Takahiro Nishiyama

Keyword(s):

Covariance Matrix ◽

High Dimensional Data ◽

High Dimensional ◽

Simultaneous Testing ◽

The Mean ◽

Mean Vector

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A simultaneous testing of the mean vector and the covariance matrix among two populations for high-dimensional data

Test ◽

10.1007/s11749-017-0567-x ◽

2017 ◽

Vol 27 (3) ◽

pp. 680-699 ◽

Cited By ~ 1

Author(s):

Masashi Hyodo ◽

Takahiro Nishiyama

Keyword(s):

Covariance Matrix ◽

High Dimensional Data ◽

High Dimensional ◽

Simultaneous Testing ◽

The Mean ◽

Mean Vector ◽

Two Populations

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A New Test on High-Dimensional Mean Vector Without Any Assumption on Population Covariance Matrix

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2012.717663 ◽

2014 ◽

Vol 43 (24) ◽

pp. 5290-5304 ◽

Cited By ~ 3

Author(s):

Shota Katayama ◽

Yutaka Kano

Keyword(s):

Covariance Matrix ◽

High Dimensional ◽

Mean Vector

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Simultaneous testing of mean vector and covariance matrix for high-dimensional data

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2017.03.009 ◽

2017 ◽

Vol 188 ◽

pp. 82-93 ◽

Cited By ~ 7

Author(s):

Zhongying Liu ◽

Baisen Liu ◽

Shurong Zheng ◽

Ning-Zhong Shi

Keyword(s):

Covariance Matrix ◽

High Dimensional Data ◽

High Dimensional ◽

Simultaneous Testing ◽

Mean Vector

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Robust Estimation of a High-Dimensional Integrated Covariance Matrix

SSRN Electronic Journal ◽

10.2139/ssrn.1996261 ◽

2012 ◽

Author(s):

Takayuki Morimoto ◽

Shuichi Nagata

Keyword(s):

Covariance Matrix ◽

Robust Estimation ◽

High Dimensional ◽

Integrated Covariance

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Small Populations, High-Dimensional Spaces: Sparse Covariance Matrix Adaptation

Proceedings of the 2015 Federated Conference on Computer Science and Information Systems ◽

10.15439/2015f261 ◽

2015 ◽

Cited By ~ 1

Author(s):

Silja Meyer-Nieberg ◽

Erik Kropat

Keyword(s):

Covariance Matrix ◽

High Dimensional ◽

Small Populations ◽

Covariance Matrix Adaptation

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Economic-statistical design of multivariate control charts for monitoring the mean vector and covariance matrix

Journal of Loss Prevention in the Process Industries ◽

10.1016/s0950-4230(02)00089-x ◽

2003 ◽

Vol 16 (1) ◽

pp. 9-18 ◽

Cited By ~ 11

Author(s):

C.-Y. Chou ◽

C.-H. Chen ◽

H.-R. Liu ◽

X.-R. Huang

Keyword(s):

Covariance Matrix ◽

Control Charts ◽

Statistical Design ◽

Multivariate Control Charts ◽

The Mean ◽

Multivariate Control ◽

Mean Vector

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Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings

Journal of the American Statistical Association ◽

10.1080/01621459.2012.758041 ◽

2013 ◽

Vol 108 (501) ◽

pp. 265-277 ◽

Cited By ~ 69

Author(s):

Tony Cai ◽

Weidong Liu ◽

Yin Xia

Keyword(s):

Covariance Matrix ◽

Sample Covariance Matrix ◽

High Dimensional ◽

Sample Covariance ◽

Support Recovery

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ON DETERMINING THE NUMBER OF SPIKES IN A HIGH-DIMENSIONAL SPIKED POPULATION MODEL

Random Matrices Theory and Application ◽

10.1142/s201032631150002x ◽

2012 ◽

Vol 01 (01) ◽

pp. 1150002 ◽

Cited By ~ 13

Author(s):

DAMIEN PASSEMIER ◽

JIAN-FENG YAO

Keyword(s):

Covariance Matrix ◽

Population Model ◽

Factor Model ◽

Fundamental Problem ◽

High Dimensional ◽

Sample Covariance ◽

Or Economics ◽

Linear Mixture Model ◽

The Difference ◽

Scientific Fields

In a spiked population model, the population covariance matrix has all its eigenvalues equal to units except for a few fixed eigenvalues (spikes). Determining the number of spikes is a fundamental problem which appears in many scientific fields, including signal processing (linear mixture model) or economics (factor model). Several recent papers studied the asymptotic behavior of the eigenvalues of the sample covariance matrix (sample eigenvalues) when the dimension of the observations and the sample size both grow to infinity so that their ratio converges to a positive constant. Using these results, we propose a new estimator based on the difference between two consecutive sample eigenvalues.

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