Classical and Bayesian Inference Robustness in Multivariate Regression models
AbstractSome 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 InfoPaper provided by Catholique de Louvain - Institut de statistique in its series Papers with number 9602.
Length: 18 pages
Date of creation: 1996
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Postal: Universite Catholique de Louvain, Institut de Statistique, Voie du Roman Pays, 34 B-1348 Louvain- La-Neuve, Belgique.
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- Fernández, C. & Osiewalski, J. & Steel, M.F.J., 1996.
"Robust Bayesian Inference on Scale Parameters,"
1996-65, Tilburg University, Center for Economic Research.
- 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.
- 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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