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• W2892366534 abstract "We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia." @default.
• W2892366534 created "2018-09-27" @default.
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• W2892366534 date "2017-09-06" @default.
• W2892366534 modified "2023-09-24" @default.
• W2892366534 title "Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia" @default.
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• W2892366534 doi "https://doi.org/10.1093/biostatistics/kxx042" @default.
• W2892366534 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6959469" @default.
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