Large Covariance Estimation for Compositional Data Via Composition-Adjusted Thresholding

DOI10.1080/01621459.2018.1442340zbMath1420.62240arXiv1601.04397OpenAlexW2963727293WikidataQ130149477 ScholiaQ130149477MaRDI QIDQ5231504

Wei Lin, Yuanpei Cao, Hongzhe Li

Publication date: 27 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1601.04397

zbMATH Keywords

regularizationhigh dimensionalitymicrobiomeadaptive thresholdingbasis covariancecentered log-ratio covariance

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (5)

Robust sparse precision matrix estimation for high-dimensional compositional data ⋮ Robust Shape Matrix Estimation for High-Dimensional Compositional Data with Application to Microbial Inter-Taxa Analysis ⋮ Estimating sparse networks with hubs ⋮ Robust Signal Recovery for High-Dimensional Linear Log-Contrast Models with Compositional Covariates ⋮ Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis

Uses Software

• CCLasso

Cites Work

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• High dimensional covariance matrix estimation using a factor model
• Optimal rates of convergence for sparse covariance matrix estimation
• Covariance regularization by thresholding
• Operator norm consistent estimation of large-dimensional sparse covariance matrices
• Adaptive Thresholding for Sparse Covariance Matrix Estimation
• Logistic-Normal Distributions: Some Properties and Uses
• Generalized Thresholding of Large Covariance Matrices
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements

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