A characterization of Bayesian robustness for a normal location parameter

Kamlesh Kumar, Jan R. Magnus*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


We consider the Bayesian estimation of a location parameter θ based on one observation x from a univariate normal distribution with mean θ and variance one, together with a prior π. In general, the mean t(x) in the posterior distribution does not satisfy the requirement that x − t(x) vanishes as x approaches ∞ (for example, when π is normal or Laplace), that is, the prior is not robust. In this paper we obtain, under mild regularity conditions on π, a necessary and sufficient (and easy to apply) condition for robustness, and identify classes of robust priors. Special attention is paid to the Subbotin prior because of its role in Bayesian model averaging.

Original languageEnglish
Pages (from-to)216-237
Number of pages22
JournalSankhya B
Issue number2
Publication statusPublished - 1 Nov 2013


Dive into the research topics of 'A characterization of Bayesian robustness for a normal location parameter'. Together they form a unique fingerprint.

Cite this