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PARTIAL INTRINSIC BAYES FACTOR

Joo Y.    (Division of Biostatistics, University of Florida   ); Casella G.    (Department of Statistics, University of Florida  );
  • 초록

    We have developed a new model selection criteria, the partial intrinsic Bayes factor, which is designed for cases when we select a model among a small number of candidate models. For example, we can choose only a few candidate models after exploring scatter plots. By simulation study, we have showed that PIBF performs better than AIC, BIC and GCV.


  • 주제어

    Bayes factor .   intrinsic Bayes factor .   Bayesian model selection.  

  • 참고문헌 (16)

    1. AITKIN, M. (1991). 'Posterior Bayes factors', Journal of the Royal Statistical Society, Ser. B, 53, 111-142 
    2. GELFAND, A. E. AND DEY, D. K. (1994). 'Bayesian model choice: asymptotics and exact calculations', Journal of the Royal Statistical Society, Ser. B, 56, 501-514 
    3. JUDGE, G. G., HILL, R. C., GRIFFITHS, W. E., LUTKEPOHL, H. AND LEE, T. C. (1988). Introduction to the Theory and Practice of Econometrics, 2nd ed., John Wiley & Sons, New York 
    4. SCHWARZ, G. (1978). 'Estimating the dimension of a model', The Annals of Statistics, 6, 461-464 
    5. ATKINSON, A. C. (1978). 'Posterior probabilities for choosing a regression model' , Biometrika, 65, 39-48 
    6. BRUMBACK, B. A., RUPPERT, D. AND WAND, M. P. (1999). Comment on 'Variable selection and function estimation in additive nonparametric regression using a data-based prior' (by Shively, T. S. et al.), Journal of the American Statistical Association, 94, 794-797 
    7. BERGER, J. O. AND PERICCHI, L. R. (1996). 'The intrinsic Bayes factor for linear models', In Bayesian Statistics 5 (Bernardo, J. M. et al., eds.), 23-42, Oxford University Press, New York 
    8. HASTIE, T., TIBSHIRANI, R. AND FRIEDMAN, J. (2001). The Elements of Statistical Learning, Springer-Verlag, New York 
    9. GEISSER, S. AND EDDY, W. F. (1979). 'A predictive approach to model selection', Journal of the American Statistical Association, 74, 153-160 
    10. AKAIKE, H. (1973). 'Information theory and an extension of the maximum likelihood principle', In Second International Symposium on Information Theory (Petrov, B. N. and Csaki, F., eds.), 267-281 
    11. DUDOIT, S., YANG, Y. H., CALLOW, M. J. AND SPEED, T. P. (2002). 'Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments', Statistica Sinica, 12, 111-139 
    12. CASELLA, G. (2001). 'Empirical Bayes Gibbs sampling', Biostatistics, 2, 485-500 
    13. RUPPERT, D., WAND, M. P. AND CAROLL, R. J. (2003). Semiparametric Regression, Cambridge University Press, Cambridge 
    14. Joo, Y. (2003). Evaluation of Model Selection Criteria in Log Spline Models, Ph. D. Dissertation, Cornell University, New York 
    15. O'HAGAN, A. (1991). Discussion on 'Posterior Bayes factors' (by Aitkin, M.), Journal of Royal Statistic Society, Ser. B, 53, 136 
    16. PENA, D. AND TIAO, G. C. (1992). 'Bayesian robustness functions for linear models', In Bayesian Statistics 4 (Bernardo, J. M. et al. eds.), 365-389, Oxford University Press, New York 

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