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The Gaussian graphical model, a popular paradigm for studying relationship among variables in a wide range of applications, has attracted great attention in recent years.
Thorin, G. O.G. O. (1948). Convexity theorems generalizing those of M. Riesz and Hadamard with some applications. Comm. Sem. Math. Univ. Lund [Medd. Lunds Univ. Mat. Sem.] 9 1–58
1948
Earlier work this paper cites.
Chandrasekaran, VenkatV., Parrilo, Pablo A.P. A. andWillsky, Alan S.A. S. (2012). Latent variable graphical model selection via convex optimization. Ann. Statist. 40 1935–1967
1967
Earlier work this paper cites.
Le Cam, L.L. (1973). Convergence of estimates under dimensionality restrictions. Ann. Statist. 1 38–53
1973
Earlier work this paper cites.
Horn, Roger A.R. A. andJohnson, Charles R.C. R. (1990). Matrix Analysis. Cambridge Univ. Press, Cambridge
1990
Earlier work this paper cites.
Lauritzen, Steffen L.S. L. (1996). Graphical Models. Oxford Statistical Science Series 17. Oxford Univ. Press, New York
1996
Earlier work this paper cites.
Ren, ZhaoZ. andZhou, Harrison H.H. H. (2012). Discussion: Latent variable graphical model selection via convex optimization [MR3059067]. Ann. Statist. 40 1989–1996
1996
Earlier work this paper cites.
Yu, B.B. (1997). Assouad, Fano, and Le Cam. In Festschrift for Lucien Le Cam
1997
Earlier work this paper cites.
Meinshausen, NicolaiN. andBühlmann, PeterP. (2006). High-dimensional graphs and variable selection with the lasso. Ann. Statist. 34 1436–1462
2006
Earlier work this paper cites.
Yuan, MingM. andLin, YiY. (2007). Model selection and estimation in the Gaussian graphical model. Biometrika 94 19–35
2007
Earlier work this paper cites.
d’Aspremont, AlexandreA., Banerjee, OnureenaO. andEl Ghaoui, LaurentL. (2008). First-order methods for sparse covariance selection. SIAM J. Matrix Anal. Appl. 30 56–66
2008
Earlier work this paper cites.
El Karoui, NoureddineN. (2008). Operator norm consistent estimation of large-dimensional sparse covariance matrices. Ann. Statist. 36 2717–2756
2008
Earlier work this paper cites.
Friedman, JeromeJ., Hastie, TrevorT. andTibshirani, RobertR. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9 432–441
2008
Earlier work this paper cites.
Rothman, Adam J.A. J., Bickel, Peter J.P. J., Levina, ElizavetaE. andZhu, JiJ. (2008). Sparse permutation invariant covariance estimation. Electron. J. Stat. 2 494–515
2008
Earlier work this paper cites.
Zhang, Cun-HuiC.-H. andHuang, JianJ. (2008). The sparsity and bias of the LASSO selection in high-dimensional linear regression. Ann. Statist. 36 1567–1594
2008
Earlier work this paper cites.
Bickel, Peter J.P. J., Ritov, Ya’acovY. andTsybakov, Alexandre B.A. B. (2009). Simultaneous analysis of lasso and Dantzig selector. Ann. Statist. 37 1705–1732
2009
Earlier work this paper cites.
Candès, Emmanuel J.E. J. andRecht, BenjaminB. (2009). Exact matrix completion via convex optimization. Found. Comput. Math. 9 717–772
2009
Cited alongside, same era.
Koltchinskii, VladimirV. (2009). The Dantzig selector and sparsity oracle inequalities. Bernoulli 15 799–828
2009
Cited alongside, same era.
Lam, CliffordC. andFan, JianqingJ. (2009). Sparsistency and rates of convergence in large covariance matrix estimation. Ann. Statist. 37 4254–4278
2009
Cited alongside, same era.
van de Geer, Sara A.S. A. andBühlmann, PeterP. (2009). On the conditions used to prove oracle results for the Lasso. Electron. J. Stat. 3 1360–1392
2009
Cited alongside, same era.
Zhang, TongT. (2009). Some sharp performance bounds for least squares regression with L 1 L_{1} regularization. Ann. Statist. 37 2109–2144
2009
Cited alongside, same era.
Ravikumar, PradeepP., Wainwright, Martin J.M. J., Raskutti, GarveshG. andYu, BinB. (2011). High-dimensional covariance estimation by minimizing ℓ 1 \ell_{1} -penalized log-determinant divergence. Electron. J. Stat. 5 935–980
2011
Later among the works it cites.
Zhang, Cun-HuiC.-H. (2011). Statistical inference for high-dimensional data. In Mathematisches Forschungsinstitut Oberwolfach: Very High Dimensional Semiparametric Models
2011
Later among the works it cites.
2012
Later among the works it cites.
Cai, T. TonyT. T. andZhou, Harrison H.H. H. (2012). Optimal rates of convergence for sparse covariance matrix estimation. Ann. Statist. 40 2389–2420
2012
Later among the works it cites.
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Antoniadis, AnestisA. (2010). Comment: ℓ 1 \ell_{1} -penalization for mixture regression models [MR2677722]. TEST 19 257–258
2010
Cited alongside, same era.
Cai, T. TonyT. T., Zhang, Cun-HuiC.-H. andZhou, Harrison H.H. H. (2010). Optimal rates of convergence for covariance matrix estimation. Ann. Statist. 38 2118–2144
2010
Cited alongside, same era.
Raskutti, GarveshG., Wainwright, Martin J.M. J. andYu, BinB. (2010). Restricted eigenvalue properties for correlated Gaussian designs. J. Mach. Learn. Res. 11 2241–2259
2010
Cited alongside, same era.
Städler, NicolasN., Bühlmann, PeterP. andvan de Geer, SaraS. (2010). ℓ 1 \ell_{1} -penalization for mixture regression models. TEST 19 209–256
2010
Cited alongside, same era.
Sun, TingniT. andZhang, Cun-HuiC.-H. (2010). Comment: ℓ 1 \ell_{1} -penalization for mixture regression models [MR2677722]. TEST 19 270–275
2010
Cited alongside, same era.
Ye, FeiF. andZhang, Cun-HuiC.-H. (2010). Rate minimaxity of the Lasso and Dantzig selector for the ℓ q \ell_{q} loss in ℓ r \ell_{r} balls. J. Mach. Learn. Res. 11 3519–3540
2010
Cited alongside, same era.
Yuan, MingM. (2010). High dimensional inverse covariance matrix estimation via linear programming. J. Mach. Learn. Res. 11 2261–2286
2010
Cited alongside, same era.
Zhang, Cun-HuiC.-H. andZhang, TongT. (2012). A general theory of concave regularization for high-dimensional sparse estimation problems. Statist. Sci. 27 576–593
2012
Later among the works it cites.
Bühlmann, PeterP. (2013). Statistical significance in high-dimensional linear models. Bernoulli 19 1212–1242
2013
Closest in time.
Liu, WeidongW. (2013). Gaussian graphical model estimation with false discovery rate control. Ann. Statist. 41 2948–2978
2013
Closest in time.
Sun, TingniT. andZhang, Cun-HuiC.-H. (2013). Sparse matrix inversion with scaled lasso. J. Mach. Learn. Res. 14 3385–3418
2013
Closest in time.
Belloni, AlexandreA., Chernozhukov, VictorV. andHansen, ChristianC. (2014). Inference on treatment effects after selection among high-dimensional controls. Rev. Econ. Stud. 81 608–650
2014
Closest in time.
Javanmard, AdelA. andMontanari, AndreaA. (2014). Hypothesis testing in high-dimensional regression under the Gaussian random design model: Asymptotic theory. IEEE Trans. Inform. Theory 60 6522–6554
2014
Closest in time.
Pang, HaotianH., Liu, HanH. andVanderbei, RobertR. (2014). The FASTCLIME package for linear programming and large-scale precision matrix estimation in R. J. Mach. Learn. Res. 15 489–493
2014
Closest in time.
van de Geer, SaraS., Bühlmann, PeterP., Ritov, Ya’acovY. andDezeure, RubenR. (2014). On asymptotically optimal confidence regions and tests for high-dimensional models. Ann. Statist. 42 1166–1202
2014
Closest in time.
2014
Closest in time.
Ren, ZhaoZ., Sun, TingniT., Zhang, Cun-HuiC.-H. andZhou, Harrison H.H. H. (2015). Supplement to “Asymptotic normality and optimalities in estimation of large Gaussian graphical models.” DOI: \doiurl
2015
Closest in time.