Bayesian nonparametric disclosure risk estimation via mixed effects log-linear models
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Publication:2349591
DOI10.1214/15-AOAS807zbMath1454.62107arXiv1306.5995MaRDI QIDQ2349591
Cinzia Carota, Roberto Leombruni, Silvia Polettini, Maurizio Filippone
Publication date: 17 June 2015
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1306.5995
Dirichlet processconfidentialitylog-linear modelsBayesian nonparametric modelsdisclosure riskmixed effects models
Related Items (3)
Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation ⋮ Optimal disclosure risk assessment ⋮ Bayesian nonparametric disclosure risk assessment
Cites Work
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- Assessing Identification Risk in Survey Microdata Using Log-Linear Models
- Discussion of the paper: ``Sampling schemes for generalized linear Dirichlet process random effects models by M. Kyung, J. Gill, and G. Casella
- Maximum likelihood estimation in log-linear models
- On a class of Bayesian nonparametric estimates: I. Density estimates
- Ferguson distributions via Polya urn schemes
- Inference from iterative simulation using multiple sequences
- Nonparametric hierarchical Bayes via sequential imputations
- Three centuries of categorical data analysis: Log-linear models and maximum likelihood estima\-tion
- Describing disability through individual-level mixture models for multivariate binary data
- A Bayesian analysis of some nonparametric problems
- Hierarchical Dirichlet Processes
- Modeling Unobserved Sources of Heterogeneity in Animal Abundance Using a Dirichlet Process Prior
- Accurate Approximations for Posterior Moments and Marginal Densities
- 10.1162/jmlr.2003.3.4-5.993
- Bayesian Density Estimation and Inference Using Mixtures
- Estimating Identification Disclosure Risk Using Mixed Membership Models
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