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- W2096936651 abstract "The weighted likelihood can be used to make inference about one population when data from similar populations are available. The author shows heuristically that the weighted likelihood can be seen as a special case of the entropy maximization principle. This leads him to propose the minimum averaged mean squared error (MAMSE) weights. He describes an algorithm for calculating these weights and shows its convergence using the Kuhn-Tucker conditions. He explores the performance and properties of the weighted likelihood based on MAMSE weights through simulations. Poids empiriques non paramétriques pour la vraisemblance La vraisemblance pondérée permet de faire de l'inférence sur une population en incorporant des données issues de populations semblables. L'auteur montre heuristiquement que la vraisemblance pondérée peut être vue comme un cas particulier du principe d'entropie maximale. Ceci le conduit à proposer les poids EQMIM (pour erreur quadratique moyenne intégrée minimale). Il décrit un algorithme pour le calcul de ces poids et en montre la convergence grâce aux conditions de Kuhn-Tucker. Il explore la performance et les propriétés de la vraisemblance pondérée basée sur les poids EQMIM à l'aide de simulations." @default.
- W2096936651 created "2016-06-24" @default.
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- W2096936651 date "2008-09-01" @default.
- W2096936651 modified "2023-09-24" @default.
- W2096936651 title "Nonparametric adaptive likelihood weights" @default.
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- W2096936651 doi "https://doi.org/10.1002/cjs.5550360308" @default.
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