منابع مشابه
Admissible Predictive Density Estimation
Let X|μ ∼ Np(μ,vxI ) and Y |μ ∼ Np(μ,vyI ) be independent pdimensional multivariate normal vectors with common unknown mean μ. Based on observing X = x, we consider the problem of estimating the true predictive density p(y|μ) of Y under expected Kullback–Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Baye...
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Suppose we observe X ∼ Nm(Aβ, σI) and would like to estimate the predictive density p(y | β) of a future Y ∼ Nn(Bβ, σI). Evaluating predictive estimates p̂(y | x) by KullbackLeibler loss, we develop and evaluate Bayes procedures for this problem. We obtain general sufficient conditions for minimaxity and dominance of the “noninformative” uniform prior Bayes procedure. We extend these results to ...
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The problems of predictive density estimation with Kullback-Leibler loss, optimal universal data compression for MDL model selection, and the choice of priors for Bayes factors in model selection are interrelated. Research in recent years has identified procedures which are minimax for risk in predictive density estimation and for redundancy in universal data compression. Here, after reviewing ...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2008
ISSN: 0090-5364
DOI: 10.1214/07-aos506