Bayesian robustness in bidimensional models: Prior independence


Journal Article

When θ is a multidimensional parameter, the issue of prior dependence or independence of coordinates is a serious concern. This is especially true in robust Bayesian analysis; Lavine et al. (J. Amer. Statist. Assoc. 86, 964-971 (1991)) show that allowing a wide range of prior dependencies among coordinates can result in near vacuous conclusions. It is sometimes possible, however, to make confidently the judgement that the coordinates of θ are independent a priori and, when this can be done, robust Bayesian conclusions improve dramatically. In this paper, it is shown how to incorporate the independence assumption into robust Bayesian analysis involving ε{lunate}-contamination and density band classes of priors. Attention is restricted to the case θ = (θ1, θ2) for clarity, although the ideas generalize. © 1994.

Full Text

Duke Authors

Cited Authors

  • Berger, J; Moreno, E

Published Date

  • January 1, 1994

Published In

Volume / Issue

  • 40 / 2-3

Start / End Page

  • 161 - 176

International Standard Serial Number (ISSN)

  • 0378-3758

Digital Object Identifier (DOI)

  • 10.1016/0378-3758(94)90118-X

Citation Source

  • Scopus