Robust Bayesian prediction and estimation under a squared log error loss function
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.
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Volume (Year): 81 (2011)
Issue (Month): 11 (November)
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