64 citations · 201 across the 34 of their papers we have counts for
4 papers · 1 filter
Uncertainty Quantification for Multi-level Models Using the Survey-Weighted Pseudo-Posterior
Matthew R. Williams, F. Hunter McGuire, Terrance D. Savitsky
Parameter estimation and inference from complex survey samples typically focuses on global model parameters whose estimators have asymptotic properties, such as from fixed effects…
Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning
Robert Chew, Matthew R. Williams, Elan A. Segarra +3
Differential privacy (DP) is becoming increasingly important for deployed machine learning applications because it provides strong guarantees for protecting the privacy of individu…
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…