
@article{rothman_sparse_2008,
	title = {Sparse permutation invariant covariance estimation},
	volume = {2},
	issn = {1935-7524},
	language = {en},
	number = {0},
	journal = {Electron. J. Statist.},
	author = {Rothman, Adam J. and Bickel, Peter J. and Levina, Elizaveta and Zhu, Ji},
	year = {2008},
	pages = {494--515},
	file = {[Rothman.2008] Sparse permutation invariant covariance estimation.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Rothman.2008] Sparse permutation invariant covariance estimation.pdf:application/pdf}
}

@article{ledoit_improved_2003,
	title = {Improved estimation of the covariance matrix of stock returns with an application to portfolio selection},
	volume = {10},
	issn = {09275398},
	language = {en},
	number = {5},
	journal = {Journal of Empirical Finance},
	author = {Ledoit, Olivier and Wolf, Michael},
	month = dec,
	year = {2003},
	pages = {603--621}
}

@article{ledoit_well-conditioned_2004,
	title = {A well-conditioned estimator for large-dimensional covariance matrices},
	volume = {88},
	issn = {0047259X},
	language = {en},
	number = {2},
	journal = {Journal of Multivariate Analysis},
	author = {Ledoit, Olivier and Wolf, Michael},
	month = feb,
	year = {2004},
	pages = {365--411},
	file = {[Ledoit.2004] A well-conditioned estimator for large-dimensional covariance matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Ledoit.2004] A well-conditioned estimator for large-dimensional covariance matrices.pdf:application/pdf}
}

@article{cai_limiting_2011,
	title = {Limiting laws of coherence of random matrices with applications to testing covariance structure and construction of compressed sensing matrices},
	volume = {39},
	issn = {0090-5364},
	language = {en},
	number = {3},
	journal = {The Annals of Statistics},
	author = {Cai, T. Tony and Jiang, Tiefeng},
	month = jun,
	year = {2011},
	pages = {1496--1525},
	file = {[Cai.2011] Limiting laws of coherence of random matrices with applications to testing.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Cai.2011] Limiting laws of coherence of random matrices with applications to testing.pdf:application/pdf}
}

@article{lan_testing_2015,
	title = {Testing the {Diagonality} of a {Large} {Covariance} {Matrix} in a {Regression} {Setting}},
	volume = {33},
	issn = {0735-0015, 1537-2707},
	language = {en},
	number = {1},
	journal = {Journal of Business \& Economic Statistics},
	author = {Lan, Wei and Luo, Ronghua and Tsai, Chih-Ling and Wang, Hansheng and Yang, Yunhong},
	month = jan,
	year = {2015},
	pages = {76--86},
	file = {[Lan.2015] Testing the Diagonality of a Large Covariance Matrix in a Regression Setting.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Lan.2015] Testing the Diagonality of a Large Covariance Matrix in a Regression Setting.pdf:application/pdf}
}

@article{srivastava_tests_2014,
	title = {Tests for covariance matrices in high dimension with less sample size},
	volume = {130},
	issn = {0047259X},
	language = {en},
	journal = {Journal of Multivariate Analysis},
	author = {Srivastava, Muni S. and Yanagihara, Hirokazu and Kubokawa, Tatsuya},
	month = sep,
	year = {2014},
	pages = {289--309},
	file = {[Srivastava.2014] Tests for covariance matrices in high dimension with less sample size.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Srivastava.2014] Tests for covariance matrices in high dimension with less sample size.pdf:application/pdf}
}

@article{cai_optimal_2013,
	title = {Optimal hypothesis testing for high dimensional covariance matrices},
	volume = {19},
	issn = {1350-7265},
	language = {en},
	number = {5B},
	journal = {Bernoulli},
	author = {Cai, T. Tony and Ma, Zongming},
	month = nov,
	year = {2013},
	pages = {2359--2388}
}

@article{lee_maximum_2018,
	title = {Maximum {Pairwise} {Bayes} {Factors} for {Covariance} {Structure} {Testing}},
	journal = {arXiv preprint},
	url = {https://arxiv.org/abs/1809.03105},
	author = {Lee, Kyoungjae and Lin, Lizhen and Dunson, David},
	month = sep,
	year = {2018},
	keywords = {Statistics - Methodology}
}

@article{bickel_regularized_2008,
	title = {Regularized estimation of large covariance matrices},
	volume = {36},
	issn = {0090-5364},
	language = {en},
	number = {1},
	journal = {The Annals of Statistics},
	author = {Bickel, Peter J. and Levina, Elizaveta},
	month = feb,
	year = {2008},
	pages = {199--227},
	file = {[Bickel.2008] Regularized estimation of large covariance matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Bickel.2008] Regularized estimation of large covariance matrices.pdf:application/pdf}
}

@article{an_hypothesis_2014,
	title = {Hypothesis testing for band size detection of high-dimensional banded precision matrices},
	volume = {101},
	issn = {0006-3444, 1464-3510},
	language = {en},
	number = {2},
	journal = {Biometrika},
	author = {An, B. and Guo, J. and Liu, Y.},
	month = jun,
	year = {2014},
	pages = {477--483},
	file = {[An.2014] Hypothesis testing for band size detection of high-dimensional banded precision.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[An.2014] Hypothesis testing for band size detection of high-dimensional banded precision.pdf:application/pdf}
}

@article{lee_estimating_2017,
	title = {Estimating {Large} {Precision} {Matrices} via {Modified} {Cholesky} {Decomposition}},
	journal = {ArXiv e-prints},
	author = {Lee, K. and Lee, J.},
	month = jul,
	year = {2017},
	keywords = {Mathematics - Statistics Theory},
	file = {[Lee.2017] Estimating Large Precision Matrices via Modified Cholesky Decomposition.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Lee.2017] Estimating Large Precision Matrices via Modified Cholesky Decomposition.pdf:application/pdf}
}

@article{banerjee_posterior_2014,
	title = {Posterior convergence rates for estimating large precision matrices using graphical models},
	volume = {8},
	issn = {1935-7524},
	language = {en},
	number = {2},
	journal = {Electronic Journal of Statistics},
	author = {Banerjee, Sayantan and Ghosal, Subhashis},
	year = {2014},
	pages = {2111--2137},
	file = {[Banerjee.2014] Posterior convergence rates for estimating large precision matrices using.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Banerjee.2014] Posterior convergence rates for estimating large precision matrices using.pdf:application/pdf}
}

@inproceedings{banerjee_convex_2006,
	title = {Convex optimization techniques for fitting sparse {Gaussian} graphical models},
	isbn = {978-1-59593-383-6},
	language = {en},
	booktitle = {Proceedings of the 23rd international conference on {Machine} learning},
	publisher = {ACM Press},
	author = {Banerjee, Onureena and Ghaoui, Laurent El and d'Aspremont, Alexandre and Natsoulis, Georges},
	year = {2006},
	pages = {89--96},
	file = {[Banerjee.2006] Convex optimization techniques for fitting sparse Gaussian graphical models.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Banerjee.2006] Convex optimization techniques for fitting sparse Gaussian graphical models.pdf:application/pdf}
}

@article{friedman_sparse_2008,
	title = {Sparse inverse covariance estimation with the graphical lasso},
	volume = {9},
	issn = {1465-4644, 1468-4357},
	language = {en},
	number = {3},
	journal = {Biostatistics},
	author = {Friedman, J. and Hastie, T. and Tibshirani, R.},
	month = jul,
	year = {2008},
	pages = {432--441},
	file = {[Friedman.2008] Sparse inverse covariance estimation with the graphical lasso.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Friedman.2008] Sparse inverse covariance estimation with the graphical lasso.pdf:application/pdf}
}

@article{yuan_model_2007,
	title = {Model {Selection} and {Estimation} in the {Gaussian} {Graphical} {Model}},
	volume = {94},
	issn = {00063444},
	number = {1},
	journal = {Biometrika},
	author = {Yuan, Ming and Lin, Yi},
	year = {2007},
	pages = {19--35},
	file = {[Yuan.2007] Model Selection and Estimation in the Gaussian Graphical Model.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Yuan.2007] Model Selection and Estimation in the Gaussian Graphical Model.pdf:application/pdf}
}

@article{qi_quadratically_2006,
	title = {A {Quadratically} {Convergent} {Newton} {Method} for {Computing} the {Nearest} {Correlation} {Matrix}},
	volume = {28},
	issn = {0895-4798, 1095-7162},
	language = {en},
	number = {2},
	journal = {SIAM Journal on Matrix Analysis and Applications},
	author = {Qi, Houduo and Sun, Defeng},
	month = jan,
	year = {2006},
	pages = {360--385},
	file = {[Qi.2006] A Quadratically Convergent Newton Method for Computing the Nearest Correlation.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Qi.2006] A Quadratically Convergent Newton Method for Computing the Nearest Correlation.pdf:application/pdf}
}

@article{fan_large_2013,
	title = {Large covariance estimation by thresholding principal orthogonal complements},
	volume = {75},
	issn = {13697412},
	language = {en},
	number = {4},
	journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
	author = {Fan, Jianqing and Liao, Yuan and Mincheva, Martina},
	month = sep,
	year = {2013},
	pages = {603--680},
	file = {[Fan.2013] Large covariance estimation by thresholding principal orthogonal complements.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Fan.2013] Large covariance estimation by thresholding principal orthogonal complements.pdf:application/pdf}
}

@article{zhang_nearly_2010,
	title = {Nearly unbiased variable selection under minimax concave penalty},
	volume = {38},
	issn = {0090-5364},
	language = {en},
	number = {2},
	journal = {The Annals of Statistics},
	author = {Zhang, Cun-Hui},
	month = apr,
	year = {2010},
	pages = {894--942},
	file = {[Zhang.2010] Nearly unbiased variable selection under minimax concave penalty.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Zhang.2010] Nearly unbiased variable selection under minimax concave penalty.pdf:application/pdf}
}

@article{antoniadis_wavelets_1997,
	title = {Wavelets in statistics: {A} review},
	volume = {6},
	issn = {1613-981X},
	number = {2},
	journal = {Journal of the Italian Statistical Society},
	author = {Antoniadis, A.},
	month = aug,
	year = {1997},
	pages = {97},
	file = {[Antoniadis.1997] Wavelets in statistics.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Antoniadis.1997] Wavelets in statistics.pdf:application/pdf}
}

@article{fan_variable_2001,
	title = {Variable {Selection} via {Nonconcave} {Penalized} {Likelihood} and {Its} {Oracle} {Properties}},
	volume = {96},
	issn = {01621459},
	number = {456},
	journal = {Journal of the American Statistical Association},
	author = {Fan, Jianqing and Li, Runze},
	year = {2001},
	pages = {1348--1360},
	file = {[Fan.2001] Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Fan.2001] Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties.pdf:application/pdf}
}

@article{antoniadis_regularization_2001,
	title = {Regularization of {Wavelet} {Approximations}},
	volume = {96},
	issn = {0162-1459, 1537-274X},
	language = {en},
	number = {455},
	journal = {Journal of the American Statistical Association},
	author = {Antoniadis, Anestis and Fan, Jianqing},
	month = sep,
	year = {2001},
	pages = {939--967},
	file = {[Antoniadis.2001] Regularization of Wavelet Approximations.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Antoniadis.2001] Regularization of Wavelet Approximations.pdf:application/pdf}
}

@article{donoho_wavelet_1995,
	title = {Wavelet {Shrinkage}: {Asymptopia}?},
	volume = {57},
	issn = {00359246},
	number = {2},
	journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
	author = {Donoho, David L. and Johnstone, Iain M. and Kerkyacharian, Gerard and Picard, Dominique},
	year = {1995},
	pages = {301--369},
	file = {[Donoho.1995] Wavelet Shrinkage.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Donoho.1995] Wavelet Shrinkage.pdf:application/pdf}
}

@incollection{forstner_metric_2003,
	address = {Berlin, Heidelberg},
	title = {A {Metric} for {Covariance} {Matrices}},
	isbn = {978-3-642-07733-3 978-3-662-05296-9},
	language = {en},
	booktitle = {Geodesy-{The} {Challenge} of the 3rd {Millennium}},
	publisher = {Springer Berlin Heidelberg},
	author = {Förstner, Wolfgang and Moonen, Boudewijn},
	editor = {Grafarend, Erik W. and Krumm, Friedrich W. and Schwarze, Volker S.},
	year = {2003},
	pages = {299--309},
	file = {[Förstner.2003] A Metric for Covariance Matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Förstner.2003] A Metric for Covariance Matrices.pdf:application/pdf}
}

@article{barnard_modeling_2000,
	title = {Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage},
	volume = {10},
	issn = {1017-0405},
	abstract = {The covariance matrix plays an important role in statistical inference, yet modeling a covariance matrix is often a difficult task in practice due to its dimensionality and the non-negative definite constraint. In order to model a covariance matrix effectively, it is typically broken down into components based on modeling considerations or mathematical convenience. Decompositions that have received recent research attention include variance components, spectral decomposition, Cholesky decomposition, and matrix logarithm. In this paper we study a statistically motivated decomposition which appears to be relatively unexplored for the purpose of modeling. We model a covariance matrix in terms of its corresponding standard deviations and correlation matrix. We discuss two general modeling situations where this approach is useful: shrinkage estimation of regression coefficients, and a general location-scale model for both categorical and continuous variables. We present some simple choices for priors in terms of standard deviations and the correlation matrix, and describe a straightforward computational strategy for obtaining the posterior of the covariance matrix. We apply our method to real and simulated data sets in the context of shrinkage estimation.},
	number = {4},
	journal = {Statistica Sinica},
	author = {Barnard, John and McCulloch, Robert and Meng, Xiao Li},
	year = {2000},
	keywords = {General location model, General location-scale model, Gibbs sampler, Hierarchical models, Markov chain Monte Carlo, Wishart distribution},
	pages = {1281--1311}
}

@article{cai_adaptive_2011,
	title = {Adaptive {Thresholding} for {Sparse} {Covariance} {Matrix} {Estimation}},
	volume = {106},
	issn = {0162-1459, 1537-274X},
	language = {en},
	number = {494},
	journal = {Journal of the American Statistical Association},
	author = {Cai, Tony and Liu, Weidong},
	month = jun,
	year = {2011},
	pages = {672--684},
	file = {[Cai.2011] Adaptive Thresholding for Sparse Covariance Matrix Estimation.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Cai.2011] Adaptive Thresholding for Sparse Covariance Matrix Estimation.pdf:application/pdf}
}

@article{alvarez_bayesian_2014,
	title = {Bayesian inference for a covariance matrix},
	journal = {ArXiv e-prints},
	author = {Alvarez, I. and Niemi, J. and Simpson, M.},
	year = {2014},
	keywords = {Statistics - Methodology}
}

@article{dryden_non-euclidean_2009,
	title = {Non-{Euclidean} statistics for covariance matrices, with applications to diffusion tensor imaging},
	volume = {3},
	issn = {1932-6157},
	language = {en},
	number = {3},
	journal = {The Annals of Applied Statistics},
	author = {Dryden, Ian L. and Koloydenko, Alexey and Zhou, Diwei},
	month = sep,
	year = {2009},
	pages = {1102--1123},
	file = {[Dryden.2009] Non-Euclidean statistics for covariance matrices, with applications to.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Dryden.2009] Non-Euclidean statistics for covariance matrices, with applications to.pdf:application/pdf}
}

@article{cai_estimating_2016,
	title = {Estimating structured high-dimensional covariance and precision matrices: {Optimal} rates and adaptive estimation},
	volume = {10},
	issn = {1935-7524},
	shorttitle = {Estimating structured high-dimensional covariance and precision matrices},
	language = {en},
	number = {1},
	journal = {Electronic Journal of Statistics},
	author = {Cai, T. Tony and Ren, Zhao and Zhou, Harrison H.},
	year = {2016},
	pages = {1--59},
	file = {[Cai.2016] Estimating structured high-dimensional covariance and precision matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Cai.2016] Estimating structured high-dimensional covariance and precision matrices.pdf:application/pdf}
}

@article{engel_overview_2017,
	title = {An overview of large-dimensional covariance and precision matrix estimators with applications in chemometrics},
	volume = {31},
	issn = {08869383},
	language = {en},
	number = {4},
	journal = {Journal of Chemometrics},
	author = {Engel, Jasper and Buydens, Lutgarde and Blanchet, Lionel},
	month = apr,
	year = {2017},
	pages = {e2880},
	file = {[Engel.2017] An overview of large-dimensional covariance and precision matrix estimators.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Engel.2017] An overview of large-dimensional covariance and precision matrix estimators.pdf:application/pdf}
}

@inproceedings{cherian_efficient_2011,
	title = {Efficient similarity search for covariance matrices via the {Jensen}-{Bregman} {LogDet} {Divergence}},
	isbn = {978-1-4577-1102-2 978-1-4577-1101-5 978-1-4577-1100-8},
	booktitle = {2011 {International} {Conference} on {Computer} {Vision}},
	publisher = {IEEE},
	author = {Cherian, Anoop and Sra, Suvrit and Banerjee, Arindam and Papanikolopoulos, Nikolaos},
	month = nov,
	year = {2011},
	pages = {2399--2406},
	file = {[Cherian.2011] Efficient similarity search for covariance matrices via the Jensen-Bregman.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Cherian.2011] Efficient similarity search for covariance matrices via the Jensen-Bregman.pdf:application/pdf}
}

@incollection{moakher_symmetric_2006,
	address = {Berlin, Heidelberg},
	title = {Symmetric {Positive}-{Definite} {Matrices}: {From} {Geometry} to {Applications} and {Visualization}},
	isbn = {978-3-540-25032-6 978-3-540-31272-7},
	shorttitle = {Symmetric {Positive}-{Definite} {Matrices}},
	booktitle = {Visualization and {Processing} of {Tensor} {Fields}},
	publisher = {Springer Berlin Heidelberg},
	author = {Moakher, Maher and Batchelor, Philipp G.},
	editor = {Weickert, Joachim and Hagen, Hans},
	year = {2006},
	pages = {285--298},
	file = {[Moakher.2006] Symmetric Positive-Definite Matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Moakher.2006] Symmetric Positive-Definite Matrices.pdf:application/pdf}
}

@article{arsigny_log-euclidean_2006,
	title = {Log-{Euclidean} metrics for fast and simple calculus on diffusion tensors},
	volume = {56},
	issn = {0740-3194, 1522-2594},
	language = {en},
	number = {2},
	journal = {Magnetic Resonance in Medicine},
	author = {Arsigny, Vincent and Fillard, Pierre and Pennec, Xavier and Ayache, Nicholas},
	month = aug,
	year = {2006},
	pages = {411--421},
	file = {[Arsigny.2006] Log-Euclidean metrics for fast and simple calculus on diffusion tensors.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Arsigny.2006] Log-Euclidean metrics for fast and simple calculus on diffusion tensors.pdf:application/pdf}
}

@article{fan_overview_2016,
	title = {An overview of the estimation of large covariance and precision matrices: {Large} covariance estimation},
	volume = {19},
	issn = {13684221},
	language = {en},
	number = {1},
	journal = {The Econometrics Journal},
	author = {Fan, Jianqing and Liao, Yuan and Liu, Han},
	month = feb,
	year = {2016},
	pages = {C1--C32},
	file = {[Fan.2016] An overview of the estimation of large covariance and precision matrices.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Fan.2016] An overview of the estimation of large covariance and precision matrices.pdf:application/pdf}
}

@article{bickel_covariance_2008,
	title = {Covariance regularization by thresholding},
	volume = {36},
	issn = {0090-5364},
	language = {en},
	number = {6},
	journal = {The Annals of Statistics},
	author = {Bickel, Peter J. and Levina, Elizaveta},
	month = dec,
	year = {2008},
	pages = {2577--2604},
	file = {[Bickel.2008] Covariance regularization by thresholding.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Bickel.2008] Covariance regularization by thresholding.pdf:application/pdf}
}

@article{ledoit_honey_2004,
	title = {Honey, {I} {Shrunk} the {Sample} {Covariance} {Matrix}},
	volume = {30},
	issn = {0095-4918},
	number = {4},
	journal = {The Journal of Portfolio Management},
	author = {Ledoit, Olivier and Wolf, Michael},
	year = {2004},
	pages = {110--119}
}

@article{chen_shrinkage_2010,
	title = {Shrinkage {Algorithms} for {MMSE} {Covariance} {Estimation}},
	volume = {58},
	issn = {1053-587X, 1941-0476},
	number = {10},
	journal = {IEEE Transactions on Signal Processing},
	author = {Chen, Yilun and Wiesel, Ami and Eldar, Yonina C. and Hero, Alfred O.},
	month = oct,
	year = {2010},
	pages = {5016--5029},
	file = {[Chen.2010] Shrinkage Algorithms for MMSE Covariance Estimation.pdf:/home/kisung/Dropbox/VP. Packages/CovTools/[Chen.2010] Shrinkage Algorithms for MMSE Covariance Estimation.pdf:application/pdf}
}
