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• W2037668034 abstract "In multicenter studies, subjects in different centers may have different outcome distributions. This article is motivated by the problem of nonparametric modeling of these distributions, borrowing information across centers while also allowing centers to be clustered. Starting with a stick-breaking representation of the Dirichlet process (DP), we replace the random atoms with random probability measures drawn from a DP. This results in a nested DP prior, which can be placed on the collection of distributions for the different centers, with centers drawn from the same DP component automatically clustered together. Theoretical properties are discussed, and an efficient Markov chain Monte Carlo algorithm is developed for computation. The methods are illustrated using a simulation study and an application to quality of care in U.S. hospitals." @default.
• W2037668034 created "2016-06-24" @default.
• W2037668034 creator A5002768909 @default.
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• W2037668034 date "2008-09-01" @default.
• W2037668034 modified "2023-10-16" @default.
• W2037668034 title "The Nested Dirichlet Process" @default.
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• W2037668034 doi "https://doi.org/10.1198/016214508000000553" @default.
• W2037668034 hasPublicationYear "2008" @default.
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