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Reference Priors For Non-Normal Two-Sample Problems

Listed author(s):
  • Fernández, C.
  • Steel, M.F.J.

    (Tilburg University, Center For Economic Research)

The reference prior algorithm (Berger and Bernardo, 1992) is applied to locationscale models with any regular sampling density. A number of two-sample problems is analyzed in this general context, extending the dierence, ratio and product of Normal means problems outside Normality, while explicitly considering possibly dierent sizes for each sample. Since the reference prior turns out to be improper in all cases, we examine existence of the resulting posterior distribution and its moments under sampling from scale mixtures of Normals. In the context of an empirical example, it is shown that a reference posterior analysis is numerically feasible and can display some sensitivity to the actual sampling distributions. This illustrates the practical importance of questioning the Normality assumption.

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File URL: https://pure.uvt.nl/portal/files/527766/104.pdf
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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 1997-104.

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Date of creation: 1997
Handle: RePEc:tiu:tiucen:4592f1f6-f6e7-4af0-933f-07e7e51e2e04
Contact details of provider: Web page: http://center.uvt.nl

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  1. S. Ghosal, 1997. "Reference priors in multiparameter nonregular cases," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 6(1), pages 159-186, June.
  2. D. Stephens & A. Smith, 1992. "Sampling-resampling techniques for the computation of posterior densities in normal means problems," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 1(1), pages 1-18, December.
  3. Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
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