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Predictive density estimation: recent results

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Predictive density estimation: recent results
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20
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CC Attribution - NonCommercial - NoDerivatives 4.0 International:
You are free to use, copy, distribute and transmit the work or content in unchanged form for any legal and non-commercial purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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This talk will address the estimation of predictive densities and their efficiency as measured by frequentist risk. For Kullback-Leibler, α−divergence, L1 and L2 losses, we review several recent findings that bring into play improvements by scale expansion, as well as duality relationships with point estimation and point prediction problems. A range of models is studied and include multivariate normal with both known and unknown covariance structure, scale mixture of normals, Gamma, as well as models with restrictions on the parameter space.