Abramowitz, M. and Stegun, I. A. (Eds.) (1964) “Handbook of Mathematical Functions
with Formulas, Graphs and Mathematical Tables,” Dover: New York.
Akaike, H. (1974): “A new look at the statistical identification model,” IEEE Transactions
on Automatic Control, 19, 716-723.
Albert, J. and Chib, S. (1993): “Bayesian Analysis of Binary and Polychotomous Response
Data,” Journal of the American Statistical Association, 88, 669-679.
Bae, K. and Mallick, B. K. (2004): “Gene selection using two-level hierarchical Bayesian
model,” Bioinformatics, 20, 3423-3430.
Barndorff-Nielsen, O. E. and Blaesild, P. (1981): “Hyperbolic distributions and ramifications:
contributions to the theory and applications,” in Statistical Distributions in Scientific
Work, Vol. 4 C. Taillie, G. Patil and B. Baldessari (ed.): , Reidal : Dorderecht.
Bernardo, J. M. and Smith, A. F. M. (1994): “Bayesian Theory,” Wiley : Chichester.
Bibby, B. M. and Sorensen, M. (2003): “Hyperbolic Processes in Finance, in Handbook of
Heavy Tailed Distributions in Finance S. Rachev (ed.): , Elsevier Science, 211-248.
Box, G. E. P. and Tiao, G. C. (1973) “Bayesian Inference in Statistical Analysis,” Wiley:
New York.
Breiman, L.(1996): “Heuristics of instability and stabilization in model selection,” Annals
of Statistics, 24, 2350-238 .
Brown, P. J., Vannucci, M. and Fearn, T. (1998): “Multivariate Bayesian variable selection
and prediction,” Journal of the Royal Statistical Society B, 60, 627-641.
Brown, P. J., Vannucci, M. and Fearn, T. (2002): “Bayes model averaging with selection of
regressors,” Journal of the Royal Statistical Society B, 64, 519-536.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977): “Maximum-likelihood from incomplete
data via the EM algorithm,” Journal of the Royal Statistical Society B, 39,
1-38.
Fan, J. and Li, R.Z. (2001): “Variable selection via nonconcave penalized likelihood and its
oracle properties,” Journal of the American Statistical Association, 96, 1348-1360.
Fan, J. and Peng, H. (2004): “Nonconcave penalized likelihood with diverging number of
parameters,” Annals of Statistics, 32, 928-961.
Figueiredo, M. A. T. and Jain, A. K. (2001): “Bayesian learning of sparse classifiers,” Proceedings
IEEE Computer Society Conference in Computer Vision and Pattern Recognition,
Vol 1, 35-41.
Figueiredo, M. A. T. (2003): “Adaptive sparseness for supervised learning,” IEEE Transactions
on Pattern Analysis and Machine Intelligence, 25, 1150-1159.
Foster, D. P. and George, E. I. (1994): “The risk inflation criterion for multiple regression,”
Annals of Statistics, 22, 1947-75.
Gelfand, A. E. and Sahu, S. K. (1999): “Identifiability, improper priors, and Gibbs sampling
for generalised linear models.” Journal of the American Statistical Association, 94,
247-253.
George, E. I. and McCulloch, R. E. (1997): “Approaches for Bayesian variable selection,”
Statistica Sinica 7, 339-373.
Gradshteyn, I. S. and Ryzik, I. M. (1980) “Tables of Integrals, Series and Products: Corrected
and Enlarged Edition,” (A. Jeffrey, Ed.) Academic Press: New York.
Johnstone, I. M. and Silverman, B. W. (2005): “Empirical Bayes selection of wavelet
thresholds,” Annals of Statistics, to appear.
Kiiveri, H. (2003): “ A Bayesian approach to variable selection when the number of variables
is very large,” In Goldstein, D.R. (Ed) “Science and Statistics: Festschrift for
Terry Speed” Institute ofMathematical Statistics Lecture Notes-Monograph Series, Vol
40, 127-143.
Knight, K. and Fu, W. (2000) “Asymptotics for lasso-type estimators”, Annals of Statistics,
28, 1356-1378.
Liu, C. H., Rubin, D. B. and Wu, Y. N. (1998): “Parameter expansion to accelerate EM:
The PX-EM algorithm,” Biometrika, 85, 755-770.
Liu, J. S. (2001): “Monte Carlo Strategies in Scientific Computing,” Springer: New York.
MacKay, D. J. C. (1994): “Bayesian methods for back-propagation networks,” In Domany,
E. et al (Eds) “Models of Neural Networks III” Chapter 6, 211-254.
McLachlan, G. J. and Peel, D. (2000): “Finite Mixture Models,” Wiley: New York.
Mallick, B. K., Ghosh, D. and Ghosh, M. (2005): “Bayesian classification of tumours by
using gene expression data,” Journal of the Royal Statistical Society B, 67, 219-234.
Meng, X. L., van Dyk, D. A. (1997): “The EM algorithm – an old folk song sung to a fast
new tune (with discussion),” Journal of the Royal Statistical Society B, 59, 511-567.
Mitchell, T.J. and Beauchamp, J. J. (1988): “Bayesian variable selection in linear regression
(with Discussion),”Journal of the American Statistical Association, 83, 1023-1036.
Schwarz, G. (1978): “Estimating the dimension of a model,” Annals of Statistics, 6, 461-
464.
Sha, N., Vannucci, M., Brown, P. J., Trower, M. K., Amphlett G., Falciani F. (2003):
“Gene selection in arthritis classification with large-scale microarray expression profiles.”
Comparative and Functional Genomics, 4, 171-181.
Tibshirani, R. (1996): “Regression shrinkage and selection via the lasso,” Journal of the
Royal Statistical Society B, 58, 267-288.
Tipping, M. E. and Faul, A. (2003): “Fast marginal likelihood maximisation for sparse
Bayesian models,” In Frey, B. and Bishop, C. M. (Eds) Proceedings 9th International
Workshop on Artificial Intelligence and Statistics, Key West, Florida.
Ueda, N. and Nakano, R. (1995): “Deterministic annealing variants of EM,” In G. Tesauro,
D. S. Tourestzky, T. K. Leen (Eds) Advances in Neural Information Processing Systems
7, 545-552, MIT Press.
Vidakovic, B. (1998): “Wavelet-Based Nonparametric Bayes Methods,” in Practical Nonparametric
and Semiparametric Bayesian Statistics D. Dey, P. Muller and D. Sinha
(eds.):, New York : Springer-Verlag, 133-156.
West, M. (2003): “Bayesian Factor regression models in the large p, small n paradigm,” In
Bernardo J. M. et al (Eds), “Bayesian Statistics 7”, 733-742: Clarendon Press: Oxford.
West, M. (1987): “On scale mixtures of normal distributions. Biometrika, 74, 646-648.
Wolfe, P. J., Godsill, S. J. and Ng, W. J. (2004): “Bayesian variable selection and regularisation
for time-frequency surface estimation,” Journal of the Royal Statistical Society,
B , 66, 575-589.
Zhang, S. and Jin, J. (1996): “Computation of Special Functions,” Wiley : New York.