Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data
type: Item
instance of: scholarly article
title: Dirichlet process mixture models for modeling and generating synthetic versions of nested categorical data
DOI: 10.1214/16-BA1047
author: Jingchen Hu
author: Jerome P. Reiter
author: Quanli Wang
published in: Bayesian Analysis
publication date: 2018-05-25T00:00:00Z
full work available at URL: https://arxiv.org/abs/1412.2282
full work available at URL: https://projecteuclid.org/euclid.ba/1485227030
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62P25
zbMATH DE Number: 6873723
zbMATH Keywords: confidentiality
zbMATH Keywords: disclosure
zbMATH Keywords: latent
zbMATH Keywords: multinomial
zbMATH Keywords: Dirichlet process
zbMATH Keywords: mixture models
zbMATH Keywords: synthetic versions of nested categorical data
describes a project that uses: BayesDA
MaRDI profile type: Publication
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description: scientific article; zbMATH DE number 6873723