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Jiang W (2006) On the consistency of Bayesian variable selection for high dimensional binary regression and classification. Neural Comput 18:2762-76 [PubMed]

References and models cited by this paper

References and models that cite this paper

Buhlmann P (2004) Boosting for high-dimensional linear models Tech Rep Mathematics Department of ETH Zurich

Donoho DL (1993) Unconditional bases are optimal bases for data compression and for statistical estimation Appl Computational Harmonic Anal 1:100-115

Ge Y, Jiang W (2005) On Consistency of Bayesian Inference with Mixtures of Logistic Regression Neural Comput 18:224-243

Ghosal S (1997) Normal approximation to the posterior distribution for generalized linear models with many covariates Math Methods Stat 6:332-348

Ghosal S (1999) Asymptotic normality of posterior distributions in high dimensional linear models Bernoulli 5:315-331

Ghosal S, Ghosh JK, van_der_Vaart AW (2000) Convergence rates of posterior distributions Ann Statist 28:500-531

Greenshtein E, Ritov Y (2004) Persistence in high-dimensional linear predictor selection and the virtue of overparameterization Bernoulli 10:971-988

Lee HK (2000) Consistency of posterior distributions for neural networks. Neural Netw 13:629-42 [PubMed]

Lee KE, Sha N, Dougherty ER, Vannucci M, Mallick BK (2003) Gene selection: a Bayesian variable selection approach. Bioinformatics 19:90-7 [PubMed]

Mccullagh P, Nelder JA (1989) Generalized linear models

Rosenblatt F (1962) Principles Of Neurodynamics

Sha N, Vannucci M, Tadesse MG, Brown PJ, Dragoni I, Davies N, Roberts TC, Contestabile A, Salmon M, Buckley C, Falciani F (2004) Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage. Biometrics 60:812-9 [Journal] [PubMed]

Shen X, Wasserman L (2001) Rates of convergence of posterior distributions Ann Statist 29:79-100

Smith M, Kohn R (1996) Nonparametric regression using Bayesian variable selection J Econometrics 75:317-343

Wasserman L (1998) Asymptotic properties of nonparametric Bayesian procedures Practical nonparametric and semiparametric bayesian statistics, Dey D:Muller P:Sinha D, ed. pp.293

Yang Y, Barron AR (1998) An asymptotic property of model selection criteria IEEE Trans Information Theory 44:95-116

Zhou X, Liu KY, Wong ST (2004) Cancer classification and prediction using logistic regression with Bayesian gene selection. J Biomed Inform 37:249-59 [Journal] [PubMed]

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