The concentration function, extending the classical notion of Lorenz curve, is well suited for comparing probability measures. Such a feature can be useful in different issues in Bayesian robustness, when a probability measure is deemed a baseline to be compared with other measures by means of their functional forms. Neighbourhood classes Γ of probability measures, including well-known ones, can be defined through the concentration function and both prior and posterior expectations of given functions of the unknown parameter are studied. The ranges of such expectations over Γ can be found, restricting the search among the extremal measures in Γ. The concentration function can be also used as a criterion to assess posterior robustness, when considering sensitivity to changes in the likelihood and the prior. © 1994.
Concentration functions and Bayesian robustness
FORTINI, SANDRA;
1994
Abstract
The concentration function, extending the classical notion of Lorenz curve, is well suited for comparing probability measures. Such a feature can be useful in different issues in Bayesian robustness, when a probability measure is deemed a baseline to be compared with other measures by means of their functional forms. Neighbourhood classes Γ of probability measures, including well-known ones, can be defined through the concentration function and both prior and posterior expectations of given functions of the unknown parameter are studied. The ranges of such expectations over Γ can be found, restricting the search among the extremal measures in Γ. The concentration function can be also used as a criterion to assess posterior robustness, when considering sensitivity to changes in the likelihood and the prior. © 1994.
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