46 citations
- U.S. National Science FoundationUS7 papers
- Vassar CollegeUS5 papers
- National Center for Science and Engineering StatisticsUS3 papers
- Amherst CollegeUS1 paper
- Educational Testing ServiceUS1 paper
- National University of SingaporeSG1 paper
- RTI InternationalUS1 paper
- University of California, Santa CruzUS1 paper
- University of IowaUS1 paper
- Western UniversityCA1 paper
16 papers
The Evolution of U.S. Retail Concentration
Dominic A. Smith, Sergio Ocampo
Increases in national concentration have been a salient feature of industry dynamics in the U.S. and have contributed to concerns about increasing market power. Yet, local trends m…
Private Tabular Survey Data Products through Synthetic Microdata Generation
Jingchen Hu, Terrance D. Savitsky, Matthew R. Williams
We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record…
Re-weighting of Vector-weighted Mechanisms for Utility Maximization under Differential Privacy
Terrance D. Savitsky, Jingchen Hu, Matthew R. Williams
We address practical implementation of a risk-weighted pseudo posterior synthesizer for microdata dissemination with a new re-weighting strategy that maximizes utility of released…
Pseudo Bayesian Estimation of One-way ANOVA Model in Complex Surveys
Terrance D. Savitsky, Matthew R. Williams, Sanvesh Srivastava
We devise survey-weighted pseudo posterior distribution estimators under two-stage informative sampling of both primary clusters and secondary nested units for a one-way analysis o…
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…