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1 Projects undertaken - Welcome to the Department of Statistics ...

1 Projects undertaken - Welcome to the Department of Statistics ...

REFERENCES 6M. Bayati

REFERENCES 6M. Bayati and A. Montanari. The lasso risk for gaussian matrices. Arxiv preprint arXiv:1008.2581, 2010.P. Bühlmann and S. Van de Geer. Statistics for High-Dimensional Data: Methods, Theory and Applications.Springer-Verlag New York Inc, 2011.T. Cai and L. Wang. Orthogonal matching pursuit for sparse signal recovery. Technical report, 2010.E. Candes and T. Tao. The dantzig selector: Statistical estimation when p is much larger than n. Ann. Statist.,35(6):2313–2351, 2007.T.M. Cover and J.A. Thomas. Elements of information theory. Wiley-Interscience, 2008.C. Huang, G.H.L. Cheang, and A.R. Barron. Risk of penalized least squares, greedy selection and l1 penalizationfor flexible function libraries. Submitted to Ann. Statist., 2008.L. Jones. A simple lemma for optimization in a hilbert space, with application to projection pursuit and neuralnet training. Ann. Statist., 20:608–613, 1992.A. Joseph. Variable selection in high dimensions with random designs and orthogonal matching pursuit. Submitted,available at arXiv:1109.0730, 2011.A. Joseph, C.M. Yrigollen, M. Eastman, J.T Chang, and E.L. Grigorenko. Genetic variation modulates the impactof schooling. In Progress.SL Lake, H. Lyon, K. Tantisira, EK Silverman, ST Weiss, NM Laird, and DJ Schaid. Estimation and tests ofhaplotype-environment interaction when linkage phase is ambiguous. Human Heredity, 55(1):56–65, 2000.G. Lugosi and N. Vayatis. On the bayes-risk consistency of regularized boosting methods. Annals of Statistics,pages 30–55, 2004.S. Mallat and S.M.Z. Zhang. Matching pursuit with time-frequency dictionaries. IEEE Trans. Signal Processing,41:3397–3415, 1993.S. Mannor, R. Meir, and T. Zhang. Greedy algorithms for classification -consistency, convergence rates, andadaptivity. J. Mach. Learn. Res., 4:713–741, December 2003. ISSN 1532-4435.Y.C. Pati, R. Rezaiifar, and PS Krishnaprasad. Orthogonal matching pursuit: Recursive function approximationwith applications to wavelet decomposition. In Conf. Rec. 27th Asilomar Conf. Sig., Sys. and Comput., pages40–44. IEEE, 1993.D.J. Schaid, C.M. Rowland, D.E. Tines, R.M. Jacobson, and G.A. Poland. Score tests for association betweentraits and haplotypes when linkage phase is ambiguous. The American Journal of Human Genetics, 70(2):425–434, 2002.R. Venkataramanan, A. Joseph, and T. Tatikonda. Superposition codes for gaussian data compression. InProgress.

REFERENCES 7M.J. Wainwright. Information-theoretic limits on sparsity recovery in the high-dimensional and noisy setting.IEEE Trans. Inform. Theory, 55(12):5728–5741, 2009a.M.J. Wainwright. Sharp thresholds for high-dimensional and noisy sparsity recovery using l 1 -constrainedquadratic programming (lasso). IEEE Trans. Inform. Theory, 55(5):2183–2202, 2009b.T. Zhang. Sequential greedy approximation for certain convex optimization problems. Information Theory, IEEETransactions on, 49(3):682–691, 2003.T. Zhang. On the consistency of feature selection using greedy least squares regression. J. Mach. Learn. Res.,10:555–568, 2009a.T. Zhang. Some sharp performance bounds for least squares regression with l1 regularization. Ann. Statist., 37(5A):2109–2144, 2009b.

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