Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data

DOI10.1214/16-BA1047MaRDI QIDQ1752012

Quanli Wang, Jerome P. Reiter, Jingchen Hu

Publication date: 25 May 2018

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1412.2282

zbMATH Keywords

Dirichlet processconfidentialitymixture modelsmultinomialdisclosurelatentsynthetic versions of nested categorical data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to social sciences (62P25)

Related Items (3)

Robust Clustering With Subpopulation-Specific Deviations ⋮ Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data ⋮ Multiple imputation: a review of practical and theoretical findings

Uses Software

• BayesDA

Cites Work

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