ArticleBiometrika2018
Robust estimation of high-dimensional covariance and precision matrices.
Article in Biometrika, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- A Class of Structured High-Dimensional Dynamic Covariance Matrices.Communications in mathematics and statistics · 2025Article
- Estimation of a genetic Gaussian network using GWAS summary data.Biometrics · 2024Article
- Are Latent Factor Regression and Sparse Regression Adequate?Journal of the American Statistical Association · 2024Article
- Identifying stationary microbial interaction networks based on irregularly spaced longitudinal 16S rRNA gene sequencing data.Frontiers in microbiomes · 2024Article
- Article
- Selective inference forJournal of machine learning research : JMLR · 2023Article
- An Efficient Greedy Search Algorithm for High-dimensional Linear Discriminant Analysis.Statistica Sinica · 2023Article
- Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis.Journal of business & economic statistics : a publication of the American Statistical Association · 2023Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Covariance estimation via fiducial inference.Statistical theory and related fields · 2021Article
- Biomarker discovery in inflammatory bowel diseases using network-based feature selection.PloS one · 2019Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption. This paper presents robust matrix estimators whose performance is guaranteed for a much richer class of distributions. The proposed estimators, under a bounded fourth moment assumption, achieve the same minimax convergence rates as do existing methods under a sub-Gaussianity assumption. Consistency of the proposed estimators is also established under the weak assumption of bounded 2 +
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Registered trials
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