A Model Selection Approach in Statistical Modeling

Abstract

-

Keywords

• -

References

1. Box, G. E. P. Robustness in the strategy of scientific model building, In Launer, R. L. & Wilkinson, G. N. (eds.) Robustness in Statistics (Academic Press, New York, 1979).
2. Gelfand, A. E. and Ghosh, S. K. Model choice: A minimum posterior predictive loss approach, Biometrika 85, 1–11, 1998.
3. Gelfand, A. E. and Dey, D. K. Bayesian Model Choice: Asymptotics and Exact Calculations, J. R. Statist. Soc. B 56, 501-514, 1994.
4. Gelman, A., Carlin, J. and Stern, A. Bayesian Data Analysis (Chapman & Hall, 2003).
5. Goutis, C. and Robert, C. P. Model choice in generalised linear models: A Bayesian approach via Kullback-Leibler projections, Biometrika 85, 29–37, 1998.
6. Hodges, J. S. and Sargent D. J. Counting degrees of freedom in hierarchical and other richly- parameterised models, Biometrika 88, 367–379, 2001.
7. Kass, R. E. and Raftery, A. E. Bayes Factors, J. Am. Statist. Assoc. 90, 773–95, 1995.
8. O’Hagan, A. Kendall’s Advanced Theory of Statistics. Volume 2B. Bayesian Inference (Arnold, London, 1994)

9. Spiegelhalter, D., Thomas, D., Best, N. and Carlin, B. P. Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models, J. R. Statist. Soc. B., 35–42, 1998.