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- W2045795433 abstract "We consider empirical Bayes estimation when the i-th component has a density, given the parameter θ i , that is proportional to a given function on (0,θ i ) which may be different for different i >=1. The common prior distribution of the 0 is estimated using nonparametric function estimation via a representation of it in terms of an average marginal density and a locally weighted average of the marginal distribution function of the data. The estimated prior is used to construct the empirical Bayes estimator. The asymptotic properties of the estimators are established using empirical processes methodology. The finite sample performance of the estimators are studied using a computer simulation. Extensions of the methods to families with threshold parameters are also provided." @default.
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- W2045795433 date "2000-01-01" @default.
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- W2045795433 title "Empirical bayes estimation with non-identical components" @default.
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- W2045795433 doi "https://doi.org/10.1080/10485250008832829" @default.
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