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Near ignorance classes of log-concave priors for the location model

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Summary

Near ignorance classes (NIC) for the normal location model are defined in Pericchi and Walley (1991) as a proper alternative to the improper uniform prior. In this paper we study the feasibility of such classes using log-concave distributions such as the logistic and the Box and Tiao exponential power family. We use Meeden and Isaacson’s theory of posterior moments for exponential family likelihoods, as well as results for posterior distributions under log-concave priors. The main conclusion that emerges is that exponential tails are the lightest permitted in order to produce NICs with non-vacuous posterior inference, for the normal location model.

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Authors and Affiliations

  1. Departmento de Matemáticas, Universidad Simón Bolívar, Apartado 89000, 1080 A, Caracas, Venezuela

    B. Sanso & L. R. Pericchi

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  1. B. Sanso
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  2. L. R. Pericchi
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Sanso, B., Pericchi, L.R. Near ignorance classes of log-concave priors for the location model. Test 1, 39–46 (1992). https://doi.org/10.1007/BF02562660

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  • DOI: https://doi.org/10.1007/BF02562660

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