Intrinsic Priors for Testing Two Normal Means with the Default Bayes Factors

  • Jongsig Bae (Department of Mathematics, SungKyunKwan University) ;
  • Kim, Hyunsoo (Department of Mathematics, SungKyunKwan University) ;
  • Kim, Seong W. (Department of Statistics, Seoul Nation University)
  • Published : 2000.12.01

Abstract

In Bayesian model selection or testing problems of different dimensions, the conventional Bayes factors with improper noninformative priors are not well defined. The intrinsic Bayes factor and the fractional Bayes factor are used to overcome such problems by using a data-splitting idea and fraction, respectively. This article addresses a Bayesian testing for the comparison of two normal means with unknown variance. We derive proper intrinsic priors, whose Bayes factors are asymptotically equivalent to the corresponding fractional Bayes factor. We demonstrate our results with two examples.

Keywords

References

  1. Statistical Decision Theory Berger, J. O.
  2. in Bayesian Statistics 4,(Bernardo, J. M., Berger, J. O., Dawid, A. P. and Smith, A. F. M. eds) On the development of the reference priors Berger, J. O.;Bernardo, J.
  3. Journal of the American Statistical Association v.94 Default Bayes factors for Non-Nested Hypothesis Testing Berger, J. O.;Mortera, J.
  4. Journal of the American Statistical Association v.91 The intrinsic Bayes factor for model selection and preiction Berger, J. O.;Pericchi, L.
  5. In Proceedings of the Workshop on Model Selection On criticisms and comparisons of default Bayes factors for model selection and hypothesis testing Berger, J. O.;Pericchi, L. R.
  6. in Bayesian Statistics V, eds. J. M., Bernardo et al., eds. Intrinsic priors via Kullback-Leibler geometry Dmochowski, J.
  7. Journal of the American Statistical Association v.74 A predictive approach to model selection Geisser, S.;Eddy, W.F.
  8. Theory of Probability Jeffreys, H.
  9. Journal of the American Statistical Association v.90 Bayes Factors Kass, R. E.;Raftery, A.
  10. Statistics and Probability Letters v.46 Intrinsic priors for testing exponential means Kim, S. W.
  11. Jouranl of Statistical Planning and Inference Default Bayes factors for generalized linear models, to appear Kim, S. W.;Ibrahim, J. G.
  12. Ann. Inst. Statist. Math. v.49 Testing hypotheses about the power law process under failure truncation using intrinsic Bayes factors Lingham, R. T.;Sivaganesan, S.
  13. Journal of the American Statistical Association v.93 An intrinsic limiting procedure for model selection and hypothesis testing Moreno, E.;Bertolino, F.;Racugno, W.
  14. Journal of Royal Statistical Society, ser. B v.57 Fractional Bayes factors for Model Comparison O'Hagan, A.
  15. An Introduction to Probability Theory and Mathematical Statistics Rohatgi, V.
  16. Journal of Royal Statistical Society, ser. B v.46 A predictive model selection criterion San Martini, A.;Spezzaferri, S. F.
  17. Journal of Royal Statistical Society, ser. B v.44 Bayes factors for linear and log-linear models with vague prior information Spiegelhalter, D. J.;Smith, A. F. M.
  18. in Bayesian Analysis V, J. M. Bernado, et al., eds. Intrinsic Bayes factors for model selection with autoregressive data Varshavsky, J. A.