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This paper studies the estimation of high dimensional Gaussian graphical model (GGM).
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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
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Banerjee, O., Ghaoui, L.E. and d’Aspremont, A. (2008). Model selection through sparse maximum likelihood estimation. Journal of Machine Learning Research
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Liu, H., Han, F., Yuan, M., Lafferty, J. and Wasserman, L. (2012). High Dimensional Semiparametric Gaussian Copula Graphical Models. Annals of Statistics
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Liu, W. and Shao, Q.M. (2012). A Robust and Powerful Approach on Control of False Discovery Rate under Dependence. Technical report
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Sun, T. and Zhang, C.H. (2012). Scaled sparse linear regression. Biometrika
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Fan, J., Feng, Y., and Wu, Y. (2009). Network exploration via the adaptive lasso and SCAD penalties. Annals of Applied Statistics
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Yuan, M. (2010). Sparse inverse covariance matrix estimation via linear programming. Journal of Machine Learning Research
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Belloni, A., Chernozhukov, V. and Wang, L. (2011). Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika
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Cai, T. T., Liu, W. and Luo, X. (2011), A constrained ℓ 1 \ell_{1} minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association
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Ravikumar, P., Wainwright, M., Raskutti, G. and Yu, B. (2011). High-dimensional covariance estimation by minimizing l 1 l_{1} -penalized log-determinant divergence. Electronic Journal of Statistics
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Xue, L. and Zou, H. (2012). Regularized Rank-based Estimation of High-dimensional Nonparanormal Graphical Models. Annals of Statistics
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Cai, T. T., Liu, W. and Xia, Y. (2013), Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings. Journal of the American Statistical Association
2013
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2013
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Liu, W. (2013). Supplemental material to ”Gaussian graphical model estimation with false discovery rate control”
2013
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