16 citations · 67 across the 20 of their papers we have counts for
17 papers · 1 filter
Design Effect Ratios for Bayesian Survey Models: A Diagnostic Framework for Identifying Survey-Sensitive Parameters
JoonHo Lee, Matthew R. Williams, Terrance D. Savitsky
Bayesian hierarchical models fitted to complex survey data often use weighted pseudo-posteriors, followed by a correction to match a design-based sandwich covariance. Correcting ev…
Privacy Amplification for Synthetic data using Range Restriction
Jingchen Hu, Matthew R. Williams, Terrance D. Savitsky
We introduce a new class of range restricted formal data privacy standards that condition on owner beliefs about sensitive data ranges. By incorporating this additional information…
Representative dietary behavior patterns and associations with cardiometabolic outcomes in Puerto Rico using a Bayesian latent class analysis for non-probability samples
Stephanie M. Wu, Abrania Marrero, Matthew R. Williams +4
There is limited understanding of how dietary behaviors cluster together and influence cardiometabolic health at a population level in Puerto Rico. Data availability is scarce, par…
Thresholding Nonprobability Units in Combined Data for Efficient Domain Estimation
Terrance D. Savitsky, Matthew R. Williams, Julie Gerrshunskaya +1
Quasi-randomization approaches estimate latent participation probabilities for units from a nonprobability / convenience sample. Estimation of participation probabilities for conve…
Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a Supervised Weighted Overfitted Latent Class Analysis
Stephanie M. Wu, Matthew R. Williams, Terrance D. Savitsky +1
Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. \sw{Analyzing diet-driven hypertensive outcomes in this demograp…
Methods for Combining Probability and Nonprobability Samples Under Unknown Overlaps
Terrance D. Savitsky, Matthew R. Williams, Julie Gershunskaya +2
Nonprobability (convenience) samples are increasingly sought to reduce the estimation variance for one or more population variables of interest that are estimated using a randomize…
Mechanisms for Global Differential Privacy under Bayesian Data Synthesis
Jingchen Hu, Matthew R. Williams, Terrance D. Savitsky
This paper introduces a new method that embeds any Bayesian model used to generate synthetic data and converts it into a differentially private (DP) mechanism. We propose an altera…
Methods for Combining Probability and Nonprobability Samples Under Unknown Overlaps
Terrance D. Savitsky, Matthew R. Williams, Julie Gershunskaya +2
Nonprobability (convenience) samples are increasingly sought to stabilize estimations for one or more population variables of interest that are performed using a randomized survey…
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…
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…
Bayesian Estimation Under Informative Sampling with Unattenuated Dependence
Matthew R. Williams, Terrance D. Savitsky
An informative sampling design leads to unit inclusion probabilities that are correlated with the response variable of interest. However, multistage sampling designs may also induc…
Bayesian Uncertainty Estimation Under Complex Sampling
Matthew R. Williams, Terrance D. Savitsky
Social and economic studies are often implemented as complex survey designs. For example, multistage, unequal probability sampling designs utilized by federal statistical agencies…
Bayesian Pairwise Estimation Under Dependent Informative Sampling
Matthew R. Williams, Terrance D. Savitsky
An informative sampling design leads to the selection of units whose inclusion probabilities are correlated with the response variable of interest. Model inference performed on the…