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Robust Bayesian inference on scale parameters. (English) Zbl 0981.62024

Summary: We represent random vectors \(Z\) that take values in \({\mathfrak R}^n- \{0\}\) as \(Z=RY\), where \(R\) is a positive random variable and \(Y\) takes values in an \((n-1)\)-dimensional space \({\mathcal Y}\). By fixing the distribution of either \(R\) or \(Y\), while imposing independence between them, different classes of distributions on \({\mathfrak R}^n\) can be generated. As examples the spherical, \(l_q\)-spherical, \(v\)-spherical and anisotropic classes can be interpreted in this unifying framework.
We present a robust Bayesian analysis on a scale parameter in the pure scale model and in the regression model. In particular, we consider robustness of posterior inference on the scale parameter when the sampling distribution ranges over classes related to those mentioned above. Some links between Bayesian and sampling-theory results are also highlighted.

MSC:

62F15 Bayesian inference
62J05 Linear regression; mixed models
60E05 Probability distributions: general theory

References:

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