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由贝叶斯检验规则导出的区间估计AI 解读

Given a family of rules for the three-decision testing problems of locating a scalar parameter θ relative to θ0, with decisions θ < θ0, θ = θ0 and θ > θ0, and given a sample x, the set S(x) = {θ0: decide θ = θ0) (usually an interval) is an intuitively appealing set estimate of θ. This paper considers the optimality of S(x) for the case in which the testing rules are Bayes. The main result is that when the losses for the testing problems have a particular structure, S(x) is…

由贝叶斯检验规则导出的区间估计AI 解读

Given a family of rules for the three-decision testing problems of locating a scalar parameter θ relative to θ0, with decisions θ < θ0, θ = θ0 and θ > θ0, and given a sample x, the set S(x) = {θ0: decide θ = θ0) (usually an interval) is an intuitively appealing set estimate of θ. This paper considers the optimality of S(x) for the case in which the testing rules are Bayes. The main result is that when the losses for the testing problems have a particular structure, S(x) is…