Fetching the paper…
Reading the bibliography…
High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n.
Wigner, Eugene P.E. P. (1955). Characteristic vectors of bordered matrices with infinite dimensions. Ann. of Math. (2) 62 548–564
1955
Earlier work this paper cites.
Pastur, L. A.L. A. (1972). The spectrum of random matrices. Teoret. Mat. Fiz. 10 102–112
1972
Earlier work this paper cites.
Mehta, Madan LalM. L. (1991). Random Matrices, 2nd ed. Academic Press, Boston, MA
1991
Earlier work this paper cites.
Girko, Vyacheslav L.V. L. (1995). Statistical Analysis of Observations of Increasing Dimension. Theory and Decision Library. Series B: Mathematical and Statistical Methods 28. Kluwer Academic, Dordrecht. Translated from the Russian
1995
Earlier work this paper cites.
Tibshirani, RobertR. (1996). Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58 267–288
1996
Earlier work this paper cites.
Chen, Scott ShaobingS. S., Donoho, David L.D. L. andSaunders, Michael A.M. A. (1998). Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20 33–61
1998
Earlier work this paper cites.
Fazel, M.M. (2002). Matrix rank minimization with applications. Ph.D. thesis, Stanford. Available at http://faculty.washington.edu/mfazel/thesis- final.pdf
2002
Earlier work this paper cites.
Greenshtein, EitanE. andRitov, Ya’acovY. (2004). Persistence in high-dimensional linear predictor selection and the virtue of overparametrization. Bernoulli 10 971–988
2004
Earlier work this paper cites.
Huang, JunzhouJ. andZhang, TongT. (2010). The benefit of group sparsity. Ann. Statist. 38 1978–2004
2004
Earlier work this paper cites.
Candes, Emmanuel J.E. J. andTao, TerenceT. (2005). Decoding by linear programming. IEEE Trans. Inform. Theory 51 4203–4215
2005
Earlier work this paper cites.
Donoho, David L.D. L. andTanner, JaredJ. (2005). Neighborliness of randomly projected simplices in high dimensions. Proc. Natl. Acad. Sci. USA 102 9452–9457 (electronic)
2005
Earlier work this paper cites.
Tibshirani, RobertR., Saunders, MichaelM., Rosset, SaharonS., Zhu, JiJ. andKnight, KeithK. (2005). Sparsity and smoothness via the fused lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 91–108
2005
Earlier work this paper cites.
Turlach, Berwin A.B. A., Venables, William N.W. N. andWright, Stephen J.S. J. (2005). Simultaneous variable selection. Technometrics 47 349–363
2005
Earlier work this paper cites.
Donoho, David L.D. L. (2006). Compressed sensing. IEEE Trans. Inform. Theory 52 1289–1306
2006
Earlier work this paper cites.
Kim, YuwonY., Kim, JinseogJ. andKim, YongdaiY. (2006). Blockwise sparse regression. Statist. Sinica 16 375–390
2006
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.
Tropp, J. A.J. A., Gilbert, A. C.A. C. andStrauss, M. J.M. J. (2006). Algorithms for simultaneous sparse approximation. Signal Process. 86 572–602. Special issue on “Sparse approximations in signal and image processing.”
2006
Earlier work this paper cites.
Yuan, MingM. andLin, YiY. (2006). Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B Stat. Methodol. 68 49–67
2006
Earlier work this paper cites.
Zhao, PengP. andYu, BinB. (2006). On model selection consistency of Lasso. J. Mach. Learn. Res. 7 2541–2563
2006
Earlier work this paper cites.
Bunea, FlorentinaF., Tsybakov, AlexandreA. andWegkamp, MartenM. (2007). Sparsity oracle inequalities for the Lasso. Electron. J. Stat. 1 169–194
2007
Earlier work this paper cites.
Bunea, FlorentinaF., Tsybakov, Alexandre B.A. B. andWegkamp, Marten H.M. H. (2007). Aggregation for Gaussian regression. Ann. Statist. 35 1674–1697
2007
Earlier work this paper cites.
Candes, EmmanuelE. andTao, TerenceT. (2007). The Dantzig selector: Statistical estimation when p p is much larger than n n . Ann. Statist. 35 2313–2351
2007
Earlier work this paper cites.
Yuan, MingM., Ekici, AliA., Lu, ZhaosongZ. andMonteiro, RenatoR. (2007). Dimension reduction and coefficient estimation in multivariate linear regression. J. R. Stat. Soc. Ser. B Stat. Methodol. 69 329–346
2007
Earlier work this paper cites.
Bach, Francis R.F. R. (2008). Consistency of the group lasso and multiple kernel learning. J. Mach. Learn. Res. 9 1179–1225
2008
Earlier work this paper cites.
Bach, Francis R.F. R. (2008). Consistency of trace norm minimization. J. Mach. Learn. Res. 9 1019–1048
2008
Earlier work this paper cites.
Baraniuk, R. G.R. G., Cevher, V.V., Duarte, M. F.M. F. andHegde, C.C. (2008). Model-based compressive sensing. Technical report, Rice Univ. Available at arXiv: \arxivurl
2008
Earlier work this paper cites.
Bickel, Peter J.P. J. andLevina, ElizavetaE. (2008). Covariance regularization by thresholding. Ann. Statist. 36 2577–2604
2008
Earlier work this paper cites.
Bunea, FlorentinaF. (2008). Honest variable selection in linear and logistic regression models via l 1 l_{1} and l 1 + l 2 l_{1}+l_{2} penalization. Electron. J. Stat. 2 1153–1194
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
Cited alongside, same era.
Koltchinskii, V.V. andYuan, M.M. (2008). Sparse recovery in large ensembles of kernel machines. In Proceedings of COLT
2008
Cited alongside, same era.
Landgrebe, D.D. (2008). Hyperspectral image data analsysis as a high-dimensional signal processing problem. IEEE Signal Processing Magazine 19 17–28
2008
Cited alongside, same era.
Lustig, M.M., Donoho, D.D., Santos, J.J. andPauly, J.J. (2008). Compressed sensing MRI. IEEE Signal Processing Magazine 27 72–82
2008
Cited alongside, same era.
Meinshausen, NicolaiN. (2008). A note on the Lasso for Gaussian graphical model selection. Statist. Probab. Lett. 78 880–884
2008
Cited alongside, same era.
Bach, FrancisF. (2010). Self-concordant analysis for logistic regression. Electron. J. Stat. 4 384–414
2010
Closest in time.
Bunea, F.F., She, Y.Y. andWegkamp, M.M. (2010). Adaptive rank penalized estimators in multivariate regression. Technical report, Florida State. Available at arXiv: \arxivurl
2010
Closest in time.
Cai, T.T. andZhou, H.H. (2010). Optimal rates of convergence for sparse covariance matrix estimation. Technical report, Wharton School of Business, Univ. Pennsylvania. Available at http://www-stat. wharton.upenn.edu/~tcai/paper/html/Sparse- Covariance-Matrix.html
2010
Closest in time.
Candes, E. J.E. J., X. Li, Y. MaY. M. andWright, J.J. (2010). Stable principal component pursuit. In IEEE International Symposium on Information Theory
2010
Closest in time.
Kakade, S. M.S. M., Shamir, O.O., Sridharan, K.K. andTewari, A.A. (2010). Learning exponential families in high-dimensions: Strong convexity and sparsity. In AISTATS
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nardi, YuvalY. andRinaldo, AlessandroA. (2008). On the asymptotic properties of the group lasso estimator for linear models. Electron. J. Stat. 2 605–633
2008
Cited alongside, same era.
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
Cited alongside, same era.
van de Geer, Sara A.S. A. (2008). High-dimensional generalized linear models and the lasso. Ann. Statist. 36 614–645
2008
Cited alongside, same era.
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
Cited alongside, same era.
Zhou, S.S., Lafferty, J.J. andWasserman, L.L. (2008). Time-varying undirected graphs. In 21st Annual Conference on Learning Theory
2008
Cited alongside, same era.
Bickel, Peter J.P. J., Brown, James B.J. B., Huang, HaiyanH. andLi, QunhuaQ. (2009). An overview of recent developments in genomics and associated statistical methods. Philos. Trans. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 367 4313–4337
2009
Cited alongside, same era.
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
Cited alongside, same era.
2010
Closest in time.
Koltchinskii, VladimirV. andYuan, MingM. (2010). Sparsity in multiple kernel learning. Ann. Statist. 38 3660–3695
2010
Closest in time.
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
Closest in time.
Ravikumar, PradeepP., Wainwright, Martin J.M. J. andLafferty, John D.J. D. (2010). High-dimensional Ising model selection using ℓ 1 \ell_{1} -regularized logistic regression. Ann. Statist. 38 1287–1319
2010
Closest in time.
Recht, BenjaminB., Fazel, MaryamM. andParrilo, Pablo A.P. A. (2010). Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev. 52 471–501
2010
Closest in time.
Agarwal, A.A., Negahban, S.S. andWainwright, M. J.M. J. (2011). Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions. Ann. Statist. 40 1171–1197
2011
Closest in time.
Chandrasekaran, V.V., Sanghavi, S.S., Parrilo, P. A.P. A. andWillsky, A. S.A. S. (2011). Rank-sparsity incoherence for matrix decomposition. SIAM J. Optimiz. 21 572–596
2011
Closest in time.
Hsu, D.D., Kakade, S. M.S. M. andZhang, T.T. (2011). Robust matrix decomposition with sparse corruptions. IEEE Trans. Inform. Theory 57 7221–7234
2011
Closest in time.
Jenatton, R.R., Mairal, J.J., Obozinski, G.G. andBach, F.F. (2011). Proximal methods for hierarchical sparse coding. J. Mach. Learn. Res. 12 2297–2334
2011
Closest in time.
McCoy, M.M. andTropp, J.J. (2011). Two proposals for robust PCA using semidefinite programming. Electron. J. Stat. 5 1123–1160
2011
Closest in time.
Negahban, S.S. andWainwright, M. J.M. J. (2011). Simultaneous support recovery in high-dimensional regression: Benefits and perils of ℓ 1 , ∞ \ell_{1,\infty} -regularization. IEEE Trans. Inform. Theory 57 3481–3863
2011
Closest in time.
Negahban, SahandS. andWainwright, Martin J.M. J. (2011). Estimation of (near) low-rank matrices with noise and high-dimensional scaling. Ann. Statist. 39 1069–1097
2011
Closest in time.
Obozinski, GuillaumeG., Wainwright, Martin J.M. J. andJordan, Michael I.M. I. (2011). Support union recovery in high-dimensional multivariate regression. Ann. Statist. 39 1–47
2011
Closest in time.
Raskutti, GarveshG., Wainwright, Martin J.M. J. andYu, BinB. (2011). Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls. IEEE Trans. Inform. Theory 57 6976–6994
2011
Closest in time.
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
Closest in time.
Recht, BenjaminB. (2011). A simpler approach to matrix completion. J. Mach. Learn. Res. 12 3413–3430
2011
Closest in time.
Rohde, AngelikaA. andTsybakov, Alexandre B.A. B. (2011). Estimation of high-dimensional low-rank matrices. Ann. Statist. 39 887–930
2011
Closest in time.
Rudelson, M.M. andZhou, S.S. (2011). Reconstruction from anisotropic random measurements. Technical report, Univ. Michigan
2011
Closest in time.
Negahban, S.S., Ravikumar, P.P., Wainwright, M. J.M. J. andYu, B.B. (2012). Supplement to “A unified framework for high-dimensional analysis of M M -estimators with decomposable regularizers.” DOI: \doiurl
2012
Closest in time.
Negahban, S.S. andWainwright, M. J.M. J. (2012). Restricted strong convexity and (weighted) matrix completion: Optimal bounds with noise. J. Mach. Learn. Res. 13 1665–1697
2012
Closest in time.
Raskutti, GarveshG., Wainwright, Martin J.M. J. andYu, BinB. (2012). Minimax-optimal rates for sparse additive models over kernel classes via convex programming. J. Mach. Learn. Res. 13 389–427
2012
Closest in time.
Xu, H.H., Caramanis, C.C. andSanghavi, S.S. (2012). Robust PCA via outlier pursuit. IEEE Trans. Inform. Theory 58 3047–3064
2012
Closest in time.
Keshavan, R. H.R. H., Montanari, A.A. andOh, S.S. (2010). Matrix completion from noisy entries. J. Mach. Learn. Res. 11 2057–2078
2078
Closest in time.