Sparse estimation of high-dimensional correlation matrices

Ying Cui; Chenlei Leng; Defeng Sun

doi:10.1016/j.csda.2014.10.001

Sparse estimation of high-dimensional correlation matrices

Ying Cui, Chenlei Leng, Defeng Sun

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.

Original language	English (US)
Pages (from-to)	390-403
Number of pages	14
Journal	Computational Statistics and Data Analysis
Volume	93
DOIs	https://doi.org/10.1016/j.csda.2014.10.001
State	Published - Jan 1 2016
Externally published	Yes

Bibliographical note

Funding Information:
The research of Sun was supported in part by the NUS Academic Research Fund under grant R-146-000-149-112 . We are grateful to two reviewers for their constructive comments.

Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.

Keywords

Accelerated proximal gradient
Correlation matrix
High-dimensionality
Positive definiteness
Sparsity

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10.1016/j.csda.2014.10.001

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title = "Sparse estimation of high-dimensional correlation matrices",

abstract = "Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.",

keywords = "Accelerated proximal gradient, Correlation matrix, High-dimensionality, Positive definiteness, Sparsity",

author = "Ying Cui and Chenlei Leng and Defeng Sun",

note = "Funding Information: The research of Sun was supported in part by the NUS Academic Research Fund under grant R-146-000-149-112 . We are grateful to two reviewers for their constructive comments. Publisher Copyright: {\textcopyright} 2014 Elsevier B.V. All rights reserved.",

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doi = "10.1016/j.csda.2014.10.001",

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T1 - Sparse estimation of high-dimensional correlation matrices

AU - Cui, Ying

AU - Leng, Chenlei

AU - Sun, Defeng

N1 - Funding Information: The research of Sun was supported in part by the NUS Academic Research Fund under grant R-146-000-149-112 . We are grateful to two reviewers for their constructive comments. Publisher Copyright: © 2014 Elsevier B.V. All rights reserved.

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.

AB - Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.

KW - Accelerated proximal gradient

KW - Correlation matrix

KW - High-dimensionality

KW - Positive definiteness

KW - Sparsity

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AN - SCOPUS:84944192107

SN - 0167-9473

VL - 93

SP - 390

EP - 403

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

ER -

Sparse estimation of high-dimensional correlation matrices

Abstract

Bibliographical note

Keywords

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