These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
223 related articles for article (PubMed ID: 27777471)
1. Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data. Cai TT; Zhang A J Multivar Anal; 2016 Sep; 150():55-74. PubMed ID: 27777471 [TBL] [Abstract][Full Text] [Related]
2. Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices. Cai T; Ma Z; Wu Y Probab Theory Relat Fields; 2015 Apr; 161(3-4):781-815. PubMed ID: 26257453 [TBL] [Abstract][Full Text] [Related]
3. Inference for High-dimensional Differential Correlation Matrices. Cai TT; Zhang A J Multivar Anal; 2016 Jan; 143():107-126. PubMed ID: 26500380 [TBL] [Abstract][Full Text] [Related]
4. ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES. Fan J; Rigollet P; Wang W Ann Stat; 2015; 43(6):2706-2737. PubMed ID: 26806986 [TBL] [Abstract][Full Text] [Related]
5. Robust estimation of high-dimensional covariance and precision matrices. Avella-Medina M; Battey HS; Fan J; Li Q Biometrika; 2018 Jun; 105(2):271-284. PubMed ID: 30337763 [TBL] [Abstract][Full Text] [Related]
6. Sparse Covariance Matrix Estimation With Eigenvalue Constraints. Liu H; Wang L; Zhao T J Comput Graph Stat; 2014 Apr; 23(2):439-459. PubMed ID: 25620866 [TBL] [Abstract][Full Text] [Related]
7. Robust covariance estimation for high-dimensional compositional data with application to microbial communities analysis. He Y; Liu P; Zhang X; Zhou W Stat Med; 2021 Jul; 40(15):3499-3515. PubMed ID: 33840134 [TBL] [Abstract][Full Text] [Related]
8. Robust Covariance Matrix Estimation for High-Dimensional Compositional Data with Application to Sales Data Analysis. Li D; Srinivasan A; Chen Q; Xue L J Bus Econ Stat; 2023; 41(4):1090-1100. PubMed ID: 38125739 [TBL] [Abstract][Full Text] [Related]
9. Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation. Lam C; Fan J Ann Stat; 2009; 37(6B):4254-4278. PubMed ID: 21132082 [TBL] [Abstract][Full Text] [Related]
10. A Comparison of Methods for Estimating the Determinant of High-Dimensional Covariance Matrix. Hu Z; Dong K; Dai W; Tong T Int J Biostat; 2017 Sep; 13(2):. PubMed ID: 28953454 [TBL] [Abstract][Full Text] [Related]
11. Principal regression for high dimensional covariance matrices. Zhao Y; Caffo B; Luo X; Electron J Stat; 2021; 15(2):4192-4235. PubMed ID: 35782590 [TBL] [Abstract][Full Text] [Related]
12. Optimal Sparse Eigenspace and Low-Rank Density Matrix Estimation for Quantum Systems. Cai T; Kim D; Song X; Wang Y J Stat Plan Inference; 2021 Jul; 213():50-71. PubMed ID: 33364672 [TBL] [Abstract][Full Text] [Related]
13. Large Covariance Estimation by Thresholding Principal Orthogonal Complements. Fan J; Liao Y; Mincheva M J R Stat Soc Series B Stat Methodol; 2013 Sep; 75(4):. PubMed ID: 24348088 [TBL] [Abstract][Full Text] [Related]
15. CANONICAL THRESHOLDING FOR NON-SPARSE HIGH-DIMENSIONAL LINEAR REGRESSION. Silin I; Fan J Ann Stat; 2022 Feb; 50(1):460-486. PubMed ID: 36148472 [TBL] [Abstract][Full Text] [Related]
16. Sparse Topic Modeling: Computational Efficiency, Near-Optimal Algorithms, and Statistical Inference. Wu R; Zhang L; Cai TT J Am Stat Assoc; 2023; 118(543):1849-1861. PubMed ID: 37771513 [TBL] [Abstract][Full Text] [Related]
17. Convex Banding of the Covariance Matrix. Bien J; Bunea F; Xiao L J Am Stat Assoc; 2016; 111(514):834-845. PubMed ID: 28042189 [TBL] [Abstract][Full Text] [Related]
18. Shrinkage estimators of large covariance matrices with Toeplitz targets in array signal processing. Zhang B; Yuan S Sci Rep; 2022 Nov; 12(1):19032. PubMed ID: 36347884 [TBL] [Abstract][Full Text] [Related]
19. Minimax Rates of Li X; Wu D Entropy (Basel); 2021 Jun; 23(6):. PubMed ID: 34198925 [TBL] [Abstract][Full Text] [Related]
20. Equivariant minimax dominators of the MLE in the array normal model. Gerard D; Hoff P J Multivar Anal; 2015 May; 137():32-49. PubMed ID: 25745274 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]