Ridge estimation of inverse covariance matrices from high-dimensional data
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References listed on IDEAS
- Ledoit, Olivier & Wolf, Michael, 2004.
"A well-conditioned estimator for large-dimensional covariance matrices,"
Journal of Multivariate Analysis,
Elsevier, vol. 88(2), pages 365-411, February.
- Wolf, Michael & Ledoit, Olivier, 2000. "A well conditioned estimator for large dimensional covariance matrices," DES - Working Papers. Statistics and Econometrics. WS 10087, Universidad Carlos III de Madrid. Departamento de Estadística.
- Peter Bickel & Bo Li & Alexandre Tsybakov & Sara Geer & Bin Yu & Teófilo Valdés & Carlos Rivero & Jianqing Fan & Aad Vaart, 2006. "Regularization in statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(2), pages 271-344, September.
- Warton, David I., 2008. "Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 340-349, March.
- Joong-Ho Won & Johan Lim & Seung-Jean Kim & Bala Rajaratnam, 2013. "Condition-number-regularized covariance estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 427-450, June.
- Daniela M. Witten & Robert Tibshirani, 2009. "Covariance‐regularized regression and classification for high dimensional problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 615-636, June.
- Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
- Ledoit, Olivier & Wolf, Michael, 2003.
"Improved estimation of the covariance matrix of stock returns with an application to portfolio selection,"
Journal of Empirical Finance,
Elsevier, vol. 10(5), pages 603-621, December.
- Wolf, Michael & Ledoit, Olivier, 2000. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," DES - Working Papers. Statistics and Econometrics. WS 10089, Universidad Carlos III de Madrid. Departamento de Estadística.
- Olivier Ledoit & Michael Wolf, 2001. "Improved estimation of the covariance matrix of stock returns with an application to portofolio selection," Economics Working Papers 586, Department of Economics and Business, Universitat Pompeu Fabra.
- Michael J. Daniels & Robert E. Kass, 2001. "Shrinkage Estimators for Covariance Matrices," Biometrics, The International Biometric Society, vol. 57(4), pages 1173-1184, December.
- Schäfer Juliane & Strimmer Korbinian, 2005. "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 4(1), pages 1-32, November.
- Gérard Letac & Hélène Massam, 2004. "All Invariant Moments of the Wishart Distribution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(2), pages 295-318, June.
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- van Wieringen, Wessel N., 2017. "On the mean squared error of the ridge estimator of the covariance and precision matrix," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 88-92.
More about this item
KeywordsGraphical modeling; High-dimensional precision matrix estimation; Multivariate normal; ℓ2-penalization; Precision matrix;
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