Publications:

  • Wang, Shuo, Joseph Feldman, and Jerome Reiter. “Differentially Private Bayesian Inference for Gaussian Copula Correlations.” Journal of Computational and Graphical Statistics, accepted.

  • Joseph Feldman and Jerome Reiter. “Outcome-Assisted Multiple Imputation of Missing Treatments.” Observational Studies 12(1): 67–90, 2026. Project MUSE.

  • Joseph Feldman, Jerome Reiter, and Daniel Kowal. “Using Auxiliary Marginal Quantiles for Gaussian Copula Models with Nonignorable Missing Data.” Bayesian Analysis 1(1): 1–29, 2025.

  • Joseph Feldman and Daniel Kowal. “Bayesian Quantile Regression with Subset Selection: A Decision Analysis Perspective.” Annals of Applied Statistics 19(3): 2294–2319, September 2025. DOI: 10.1214/25-AOAS2053.

  • Joseph Feldman and Daniel Kowal. “Nonparametric Copula Models for Multivariate, Mixed, and Missing Data.” Journal of Machine Learning Research 25(164): 1–50, 2024.
    American Statistical Association Student Paper Award: Bayesian Statistical Science Section, 2023.
    American Statistical Association Student Paper Award: Government Statistics Section, 2023; declined.

  • Mercedes Bravo, Daniel Kowal, Denise Zephyr, Joseph Feldman, Katherine Ensor, and Marie Lynn Miranda. “Spatial Variability in Relationships Between Early Childhood Lead Exposure and Standardized Test Scores in 4th Grade North Carolina Public School Students (2013–2016).” Environmental Health Perspectives 132(9): 097003, 2024. https://doi.org/10.1289/EHP13898

  • Joseph Feldman and Daniel Kowal. “Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data.” Annals of Applied Statistics 16(4): 2577–2602, 2022. https://doi.org/10.1214/22-AOAS1604
    American Statistical Association Student Paper Award: Health Policy Statistics Section, 2022.

Under Review:

Joseph Feldman and Yuqi Gu. “Identifiable Bayesian Deep Generative Copulas with Unknown Layer Widths for Data with Arbitrary Marginal Distributions”

Joseph Feldman, Mercedes Bravo, Robert Chi, and Daniel Kowal “Bayesian Decision-Analytic Quantile Regression for Linking Ambient Air Pollution and Longitudinal Academic Performance in North Carolina Children: Adverse Effects of $\text{PM}_{2.5}$ Exposure are Stronger for Lower-Performing Students”

Software

  1. CopDDE: Implementation of the DDE copula

  2. GMCImpute: Multiple imputation using the Gaussian mixture copula

  3. EHQL-Impute: Multiple imputation using the Extended Hybrid Quantile Likelihood Gaussian copula for nonignorable missing data

  4. QRSubsets: Subset selection for Bayesian quantile regression