ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2014
Sparse Covariance Matrix Estimation With Eigenvalue Constraints.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Inferring independent sets of Gaussian variables after thresholding correlations.Journal of the American Statistical Association · 2025Article
- Unveiling gene regulatory networks during cellular state transitions without linkage across time points.Scientific reports · 2024Article
- Covariance shrinkage can assess and improve functional connectomes.NeuroImage · 2022Article
- Estimating Large Correlation Matrices for International Migration.The annals of applied statistics · 2018Article
- Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2016Article
- Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.Journal of machine learning research : JMLR · 2015Article
- Calibrated Precision Matrix Estimation for High-Dimensional Elliptical Distributions.IEEE transactions on information theory · 2014Article
- Statistical analysis of big data on pharmacogenomics.Advanced drug delivery reviews · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose a new approach for estimating high-dimensional, positive-definite covariance matrices. Our method extends the generalized thresholding operator by adding an explicit eigenvalue constraint. The estimated covariance matrix simultaneously achieves sparsity and positive definiteness. The estimator is rate optimal in the minimax sense and we develop an efficient iterative soft-thresholding and projection algorithm based on the alternating direction method of multipliers. Empirically, we conduct thorough numerical experiments on simulated datasets as well as real data examples to illustrate the usefulness of our method. Supplementary materials for the article are available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.