Elementary Estimators for Sparse Covariance Matrices and other Structured Moments
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[1] N. Cressie,et al. A dimension-reduced approach to space-time Kalman filtering , 1999 .
[2] I. Johnstone. On the distribution of the largest eigenvalue in principal components analysis , 2001 .
[3] I. Amit,et al. Comprehensive mapping of long range interactions reveals folding principles of the human genome , 2011 .
[4] Noureddine El Karoui,et al. Operator norm consistent estimation of large-dimensional sparse covariance matrices , 2008, 0901.3220.
[5] Martin J. Wainwright,et al. A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers , 2009, NIPS.
[6] Bin Yu,et al. High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence , 2008, 0811.3628.
[7] P. Bickel,et al. Regularized estimation of large covariance matrices , 2008, 0803.1909.
[8] Martin J. Wainwright,et al. Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions , 2011, ICML.
[9] T. Bengtsson,et al. Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants , 2007 .
[10] Harrison H. Zhou,et al. Optimal rates of convergence for covariance matrix estimation , 2010, 1010.3866.
[11] Pradeep Ravikumar,et al. Dirty Statistical Models , 2013, NIPS.
[12] Ali Jalali,et al. A Dirty Model for Multi-task Learning , 2010, NIPS.
[13] Pablo A. Parrilo,et al. The Convex Geometry of Linear Inverse Problems , 2010, Foundations of Computational Mathematics.
[14] G. Wahba,et al. Multivariate Bernoulli distribution , 2012, 1206.1874.
[15] Ming Hu,et al. HiCNorm: removing biases in Hi-C data via Poisson regression , 2012, Bioinform..
[16] L. Mirny,et al. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data , 2013, Nature Reviews Genetics.
[17] G. Watson. Characterization of the subdifferential of some matrix norms , 1992 .
[18] J. Tropp,et al. Two proposals for robust PCA using semidefinite programming , 2010, 1012.1086.
[19] J. Lafferty,et al. High-dimensional Ising model selection using ℓ1-regularized logistic regression , 2010, 1010.0311.
[20] Terence Tao,et al. The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.
[21] Adam J. Rothman,et al. Generalized Thresholding of Large Covariance Matrices , 2009 .
[22] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[23] Sham M. Kakade,et al. Robust Matrix Decomposition With Sparse Corruptions , 2011, IEEE Transactions on Information Theory.
[24] Olivier Ledoit,et al. A well-conditioned estimator for large-dimensional covariance matrices , 2004 .
[25] Constantine Caramanis,et al. Robust PCA via Outlier Pursuit , 2010, IEEE Transactions on Information Theory.
[26] P. Bickel,et al. Covariance regularization by thresholding , 2009, 0901.3079.
[27] P. L. Combettes,et al. A proximal decomposition method for solving convex variational inverse problems , 2008, 0807.2617.
[28] Jean-Marc Azaïs,et al. Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate , 2009 .
[29] Pablo A. Parrilo,et al. Rank-Sparsity Incoherence for Matrix Decomposition , 2009, SIAM J. Optim..
[30] I. Johnstone,et al. Sparse Principal Components Analysis , 2009, 0901.4392.