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Intrinsic priors for testing exponential means

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  • Kim, Seong W.

Abstract

In Bayesian model selection or testing problems of different dimensions, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion called the intrinsic Bayes factor, overcomes such problems by using a data-splitting idea. Furthermore, the intrinsic Bayes factors are asymptotically equal to the ordinary Bayes factors computed by some reasonable proper priors called intrinsic priors. Several intrinsic priors are derived for testing the equality of two independent exponential means. We demonstrate our results with a simulated dataset.

Suggested Citation

  • Kim, Seong W., 2000. "Intrinsic priors for testing exponential means," Statistics & Probability Letters, Elsevier, vol. 46(2), pages 195-201, January.
    • Handle: RePEc:eee:stapro:v:46:y:2000:i:2:p:195-201
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    References listed on IDEAS

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    1. Rama Lingham & S. Sivaganesan, 1997. "Testing Hypotheses About the Power Law Process Under Failure Truncation Using Intrinsic Bayes Factors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(4), pages 693-710, December.
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