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Robust Bayesian Inference on Scale Parameters

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  • Fernández, Carmen
  • Osiewalski, Jacek
  • Steel, Mark F. J.

Abstract

We represent random vectors Z that take values in n-{0} as Z=RY, where R is a positive random variable and Y takes values in an (n-1)-dimensional space . By fixing the distribution of either R or Y, while imposing independence between them, different classes of distributions on n can be generated. As examples, the spherical, lq-spherical, [upsilon]-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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Multivariate Analysis.

Volume (Year): 77 (2001)
Issue (Month): 1 (April)
Pages: 54-72

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Handle: RePEc:eee:jmvana:v:77:y:2001:i:1:p:54-72

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Keywords: posterior distribution scale invariance scale model regression model;

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  1. 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.
  2. 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.
  3. Steel, M.F.J., 1991. "Bayesian Inference in Time Series," Papers 9153, Tilburg - Center for Economic Research.
  4. 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.
  5. Gupta, A. K. & Song, D., 1997. "Characterization ofp-Generalized Normality," Journal of Multivariate Analysis, Elsevier, vol. 60(1), pages 61-71, January.
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