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ReferencesAlon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., and Levine, A. J. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA, 96(12):6745–6750. Bazaraa, M. S., Sherali, H. D., and Shetty, C. M. (2006). Nonlinear Programming: Theory and Algorithms. Wiley, New Jersey, 3rd edition. MR2218478 Bickel, P. J. and Levina, E. (2004). Some theory for Fisher’s linear discriminant function, “naive Bayes”, and some alternatives when there are many more variables than observations. Bernoulli, 10(6):989–1010. MR2108040 Bickel, P. J. and Levina, E. (2007). Covariance regularization by thresholding. Ann. Statist. To appear. Bickel, P. J. and Levina, E. (2008). Regularized estimation of large covariance matrices. Ann. Statist., 36(1):199–227. MR2387969 Chaudhuri, S., Drton, M., and Richardson, T. S. (2007). Estimation of a covariance matrix with zeros. Biometrika, 94(1):199–216. MR2307904 d’Aspremont, A., Banerjee, O., and El Ghaoui, L. (2008). First-order methods for sparse covariance selection. SIAM Journal on Matrix Analysis and its Applications, 30(1):56–66. Dey, D. K. and Srinivasan, C. (1985). Estimation of a covariance matrix under Stein’s loss. Ann. Statist., 13(4):1581–1591. MR0811511 Drton, M. and Perlman, M. D. (2008). A SINful approach to Gaussian graphical model selection. J. Statist. Plann. Inference, 138(4):1179–1200. El Karoui, N. (2007). Operator norm consistent estimation of large dimensional sparse covariance matrices. Ann. Statist. To appear. Fan, J., Fan, Y., and Lv, J. (2008). High dimensional covariance matrix estimation using a factor model. Journal of Econometrics. To appear. Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. J. Amer. Statist. Assoc., 96(456):1348–1360. MR1946581 Friedman, J., Hastie, T., and Tibshirani, R. (2007). Pathwise coordinate optimization. Annals of Applied Statistics, 1(2):302–332. Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics. Pre-published online, DOI 10.1093/biostatistics/kxm045. Fu, W. (1998). Penalized regressions: the bridge versus the lasso. Journal of Computational and Graphical Statistics, 7(3):397–416. MR1646710 Furrer, R. and Bengtsson, T. (2007). Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. Journal of Multivariate Analysis, 98(2):227–255. MR2301751 Golub, G. H. and Van Loan, C. F. (1989). Matrix Computations. The John Hopkins University Press, Baltimore, Maryland, 2nd edition. MR1002570 Haff, L. R. (1980). Empirical Bayes estimation of the multivariate normal covariance matrix. Ann. Statist., 8(3):586–597. MR0568722 Huang, J., Liu, N., Pourahmadi, M., and Liu, L. (2006). Covariance matrix selection and estimation via penalised normal likelihood. Biometrika, 93(1):85–98. MR2277742 Hunter, D. R. and Li, R. (2005). Variable selection using mm algorithms. Ann. Statist., 33(4):1617–1642. MR2166557 Johnstone, I. M. (2001). On the distribution of the largest eigenvalue in principal components analysis. Ann. Statist., 29(2):295–327. MR1863961 Johnstone, I. M. and Lu, A. Y. (2004). Sparse principal components analysis. Unpublished manuscript. Kalisch, M. and Bühlmann, P. (2007). Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res., 8:613–636. Lam, C. and Fan, J. (2007). Sparsistency and rates of convergence in large covariance matrices estimation. Manuscript. Ledoit, O. and Wolf, M. (2003). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88:365–411. MR2026339 Levina, E., Rothman, A. J., and Zhu, J. (2008). Sparse estimation of large covariance matrices via a nested Lasso penalty. Annals of Applied Statistics, 2(1):245–263. Lin, S. P. and Perlman, M. D. (1985). A Monte Carlo comparison of four estimators for a covariance matrix. In Krishnaiah, P. R., editor, Multivariate Analysis, volume 6, pages 411–429. Elsevier Science Publishers. MR0822310 Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979). Multivariate Analysis. Academic Press, New York. MR0560319 Meinshausen, N. and Bühlmann, P. (2006). High dimensional graphs and variable selection with the Lasso. Ann. Statist., 34(3):1436–1462. MR2278363 Paul, D. (2007). Asymptotics of sample eigenstructure for a large dimensional spiked covariance model. Stat. Sinica, 17(4):1617–1642. MR2399865 Saulis, L. and Statulevičius, V. A. (1991). Limit Theorems for Large Deviations. Kluwer Academic Publishers, Dordrecht. MR1171883 Smith, M. and Kohn, R. (2002). Parsimonious covariance matrix estimation for longitudinal data. J. Amer. Statist. Assoc., 97(460):1141–1153. MR1951266 Wang, L., Zhu, J., and Zou, H. (2007). Hybrid huberized support vector machines for microarray classification. In ICML ’07: Proceedings of the 24th International Conference on Machine Learning, pages 983–990, New York, NY, USA. ACM Press. Wong, F., Carter, C., and Kohn, R. (2003). Efficient estimation of covariance selection models. Biometrika, 90:809–830. MR2024759 Wu, W. B. and Pourahmadi, M. (2003). Nonparametric estimation of large covariance matrices of longitudinal data. Biometrika, 90:831–844. MR2024760 Yuan, M. and Lin, Y. (2007). Model selection and estimation in the Gaussian graphical model. Biometrika, 94(1):19–35. MR2367824 |
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