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Robust Bayesian inference on scale parameters




We represent random variables Z that take values in Re^n-{0} as Z=RY, where R is a positive random variable and Y takes values in an (n-1)-dimensional space Y. By fixing the distribution of either R or Y, while imposing independence between them, different classes of distributions on Re^n can be generated. As examples, the spherical, l[q]-spherical, v-spherical and anisotropic classes can be interpreted in this unifying framework. We present a robust Bayesian analysis on a scale parameter in the pure scale model and in the regression model. In particular, we consider robustness of posterior inference on the scale parameter when the sampling distribution ranges over classes related to those mentioned above. Some links between Bayesian and sampling-theory results are also highlighted.

Suggested Citation

  • Carmen Fernandez & Jacek Osiewalski & Mark F J Steel, 1996. "Robust Bayesian inference on scale parameters," ESE Discussion Papers 25, Edinburgh School of Economics, University of Edinburgh.
  • Handle: RePEc:edn:esedps:25

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    References listed on IDEAS

    1. Fang, Kai-Tai & Bentler, P. M., 1991. "A largest characterization of spherical and related distributions," Statistics & Probability Letters, Elsevier, vol. 11(2), pages 107-110, February.
    2. Gupta, A. K. & Song, D., 1997. "Characterization ofp-Generalized Normality," Journal of Multivariate Analysis, Elsevier, vol. 60(1), pages 61-71, January.
    3. Fang, Kai-Tai & Li, Runze, 1999. "Bayesian Statistical Inference on Elliptical Matrix Distributions," Journal of Multivariate Analysis, Elsevier, vol. 70(1), pages 66-85, July.
    4. Fernandez, C & Osiewalski, J & Steel, M-F-J, 1996. "Classical and Bayesian Inference Robustness in Multivariate Regression models," Papers 9602, Catholique de Louvain - Institut de statistique.
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