Biomedical Engineering Reference
In-Depth Information
342.
32. D. M. Malioutov, M. Cetin, and A. S. Willsky, Homotopy continuation for
sparse signal representation, In IEEE International Conference on Acoustics,
Speech, and Signal Processing, 5, pp. 733-736, Philadelphia, PA, March 2005.
33. S. Mallat, A wavelet tour of signal processing, Academic Press, Inc., San
Diego, CA, 1998.
34. S. Mallat and Z. Zhang, Matching pursuit in a time-frequency dictionary.
IEEE Trans. Signal Proc., 41 (1993), 3397-3415.
35. C. L. Mallows, Some comments on C p , Technometrics, 15 (1973), 661-675.
36. A. J. Miller, Subset selection in regression, Chapman and Hall, New York,
1990.
37. B. K. Natarajan, Sparse approximate solutions to linear systems, SIAM Jour-
nal on Computing, 24 (1995), 227-234.
38. X. S. Ni and X. Huo, Regression by enhanced leaps-and-bounds meth-
ods via additional optimality tests (LBOT), Working paper, available in
http://www.isye.gatech.edu/statistics/papers/, August 2005.
39. M. R. Osborne, B. Presnell, and B. Turlach, On the Lasso and its dual,
Journal of Computational and Graphical Statistics, 9 (2000), 319{337.
40. M. R. Osborne, B. Presnell, and B. A. Turlach, A new approach to variable
selection in least squares problems, IMA J. Numer. Anal., 20 (2000), 389{
403.
41. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, Orthogonal matching pur-
suit: Recursive function approximation with applications to wavelet decom-
position, In Proc. 27th Asilomar Conference on Signals, Systems and Com-
puters (A. Singh, Editor), IEEE Comput. Soc. Press, Los Alamitos, CA,
1993.
42. R. T. Rockafellar, Convex analysis, Princeton University Press, Princeton,
NJ, 1970.
43. G. Schwarz, Estimating the dimension of a model, The Annals of Statistics,
6 (1978), 461-464.
44. X. Shen and H.-C. Huang, Optimal model assessment, selection and combi-
nation, Journal of the American Statistical Association, 101 (2006), 554-568.
45. X. Shen, H.-C. Huang, and J. Ye, Inference after model selection, Journal of
the American Statistical Association, 99 (2004), 751-762.
46. X. Shen and J. M. Ye, Adaptive model selection, Journal of the American
Statistical Association, 97 (2002), 210-221.
47. R. Tibshirani, Regression shrinkage and selection via the Lasso, J. Roy.
Statist. Soc. Ser. B, 58 (1996), 267-288.
48. J. A. Tropp, Just relax: Convex programming methods for subset selection
and sparse approximation, IEEE Trans. Inform. Theory, 52 (2006), 1030-
1051.
49. J. A. Tropp, Greed is good: Algorithmic results for sparse approximation,
IEEE Trans. Inform. Theory, 50 (2004), 2231{2242.
50. S. Weisberg, Discussion of [14], The Annals of Statistics, 32 (2004), 490-494.
51. C. F. J. Wu, Construction of supersaturated designs through partially aliased
interactions, Biometrika, 80 (1993), 661-669.
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