Testing block‐diagonal covariance structure for high‐dimensional data

Masashi Hyodo, Nobumichi Shutoh, Takahiro Nishiyama, Tatjana Pavlenko

Type: Article

Publication Date: 2015-05-26

Citations: 20

DOI: https://doi.org/10.1111/stan.12068

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Statistica Neerlandica - View

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+	Testing for independence of high-dimensional variables: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3013" altimg="si5.svg"><mml:mrow><mml:mi>ρ</mml:mi><mml:mi>V</mml:mi></mml:mrow></mml:math>-coefficient based approach	2020	Masashi Hyodo Takahiro Nishiyama Tatjana Pavlenko
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+	High-dimensional inference on covariance structures via the extended cross-data-matrix methodology	2016	Kazuyoshi Yata Makoto Aoshima
+	A nonparametric test for block-diagonal covariance structure in high dimension and small samples	2019	Kai Xu Hao Xinxin
+	High-Dimensional Block Diagonal Covariance Structure Detection Using Singular Vectors	2024	Jan O. Bauer
+ PDF Chat	A component lasso	2015	Nadine Hussami Robert Tibshirani
+	Likelihood ratio tests for many groups in high dimensions	2019	Holger Dette Nina Dörnemann
+	The folded concave Laplacian spectral penalty learns block diagonal sparsity patterns with the strong oracle property	2021	Iain Carmichael

Works Cited by This (9)

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+	On testing for an identity covariance matrix when the dimensionality equals or exceeds the sample size	2011	Thomas J. Fisher
+	Some tests for the covariance matrix with fewer observations than the dimension under non-normality	2011	Muni S. Srivastava Tõnu Kollo Dietrich von Rosen
+	Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size	2001	Olivier Ledoit Michael Wolf
+	Testing for complete independence in high dimensions	2005	James R. Schott
+ PDF Chat	Some Tests Concerning the Covariance Matrix in High Dimensional Data	2005	Muni S. Srivastava
+	Covariance structure approximation via gLasso in high-dimensional supervised classification	2012	Tatjana Pavlenko Anders Björkström Annika Tillander
+	Testing the structure of the covariance matrix with fewer observations than the dimension	2012	Muni S. Srivastava Nancy Reid
+ PDF Chat	The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes	2013	Patrick Danaher Pei Wang Daniela Witten
+ PDF Chat	Some hypothesis tests for the covariance matrix when the dimension is large compared to the sample size	2002	Olivier Ledoit Michael Wolf