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- W2082501590 abstract "The Hájek (Z. Wahrch. verw. Geb. 14 (1970) 323–330) convolution theorem facilitates the study of local asymptotic minimaxity properties of regular estimators. Based on shrinking (Zellner's g-) priors, Bayes versions of such regular estimators are incorporated here in the formulation of robust Bayes and empirical Bayes estimators for a general class of parametric, semiparametric and nonparametric estimators admitting a first-order asymptotic representation. In the light of quadratic risk and generalized Pitman closeness measures, adaptive empirical Bayes estimation theory is also presented." @default.
- W2082501590 created "2016-06-24" @default.
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- W2082501590 date "2000-12-01" @default.
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- W2082501590 title "The Hájek convolution theorem and empirical Bayes estimation: parametrics, semiparametrics and nonparametrics" @default.
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- W2082501590 doi "https://doi.org/10.1016/s0378-3758(00)00198-1" @default.
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