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• W2796413144 abstract "Clustered data is ubiquitous in a variety of scientific fields. In this paper, we propose a flexible and interpretable modeling approach, called grouped heterogenous mixture modeling, for clustered data, which models cluster-wise conditional distributions by mixtures of latent conditional distributions common to all the clusters. In the model, we assume that clusters are divided into a finite number of groups and mixing proportions are the same within the same group. We provide a simple generalized EM algorithm for computing the maximum likelihood estimator, and an information criterion to select the numbers of groups and latent distributions. We also propose structured grouping strategies by introducing penalties on grouping parameters in the likelihood function. Under the settings where both the number of clusters and cluster sizes tend to infinity, we present asymptotic properties of the maximum likelihood estimator and the information criterion. We demonstrate the proposed method through simulation studies and an application to crime risk modeling in Tokyo." @default.
• W2796413144 created "2018-04-13" @default.
• W2796413144 creator A5054166002 @default.
• W2796413144 date "2018-04-03" @default.
• W2796413144 modified "2023-09-27" @default.
• W2796413144 title "Grouped Heterogeneous Mixture Modeling for Clustered Data" @default.
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• W2796413144 doi "https://doi.org/10.48550/arxiv.1804.00888" @default.
• W2796413144 hasPublicationYear "2018" @default.
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