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Classical and Bayesian Inference Robustness in Multivariate Regression models

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Author Info

  • Fernandez, C
  • Osiewalski, J
  • Steel, M-F-J

Abstract

Some classical inference procedures can be shown to be completely robust in theses classes of multivariate distributions. These findings are used in the practically relevant context of regression models. We present a robust bayesian analysis and indicate the links between classical and Bayesian results. In particular, for the regression model with i.i.d. errors up to a scale, a formal characterization is provided for both classical and Bayesian robustness results concerning inference on the regression parameters.

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Bibliographic Info

Paper provided by Catholique de Louvain - Institut de statistique in its series Papers with number 9602.

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Length: 18 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:fth:louvis:9602

Contact details of provider:
Postal: Universite Catholique de Louvain, Institut de Statistique, Voie du Roman Pays, 34 B-1348 Louvain- La-Neuve, Belgique.

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Keywords: STATISTICS;

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Cited by:
  1. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
  2. Fernández, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 2001. "Robust Bayesian Inference on Scale Parameters," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 54-72, April.
  3. Jim Smith & Fabio Rigat, 2012. "Isoseparation and robustness in parametric Bayesian inference," Annals of the Institute of Statistical Mathematics, Springer, vol. 64(3), pages 495-519, June.

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