Robust Bayesian prediction and estimation under a squared log error loss function

A. Kiapour and N. Nematollahi

Statistics & Probability Letters, 2011, vol. 81, issue 11, 1717-1724

Abstract: Robust Bayesian analysis is concerned with the problem of making decisions about some future observation or an unknown parameter, when the prior distribution belongs to a class [Gamma] instead of being specified exactly. In this paper, the problem of robust Bayesian prediction and estimation under a squared log error loss function is considered. We find the posterior regret [Gamma]-minimax predictor and estimator in a general class of distributions. Furthermore, we construct the conditional [Gamma]-minimax, most stable and least sensitive prediction and estimation in a gamma model. A prequential analysis is carried out by using a simulation study to compare these predictors.

Keywords: Class; of; priors; Gamma; distribution; Robust; Bayesian; prediction; Sensitivity; analysis; Squared; log; error; loss; function (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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