64 citations · 201 across the 34 of their papers we have counts for
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Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy
Terrance D. Savitsky, Matthew R. Williams, Jingchen Hu
We propose a Bayesian pseudo posterior mechanism to generate record-level synthetic databases equipped with an probabilistic differential privacy (pDP) guarantee, where $δ…
Risk-Efficient Bayesian Data Synthesis for Privacy Protection
Jingchen Hu, Terrance D. Savitsky, Matthew R. Williams
Statistical agencies utilize models to synthesize respondent-level data for release to the public for privacy protection. In this work, we efficiently induce privacy protection int…
Pseudo Bayesian Mixed Models under Informative Sampling
Terrance D. Savitsky, Matthew R. Williams
When random effects are correlated with sample design variables, the usual approach of employing individual survey weights (constructed to be inversely proportional to the unit sur…
Optimization of Survey Weights under a Large Number of Conflicting Constraints
Matthew R. Williams, Terrance D. Savitsky
In the analysis of survey data, sampling weights are needed for consistent estimation of the population. However, the original inverse probability weights from the survey sample de…
Bayesian Pseudo Posterior Synthesis for Data Privacy Protection
Jingchen Hu, Terrance D. Savitsky
Statistical agencies utilize models to synthesize respondent-level data for release to the general public as an alternative to the actual data records. A Bayesian model synthesizer…