Publications:

1. Joseph Feldman, Daniel R. Kowal “Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data” The Annals of Applied Statistics, Ann. Appl. Stat. 16(4), 2577-2602, (December 2022)

2. Joseph Feldman, Daniel R. Kowal. “Nonparametric copula models for multivariate, mixed, and missing data”. Journal of Machine Learning Research; 25(164): 1-50, 2024.

3. Bravo, M., Kowal, D., Zephyr, D., Feldman, J., Ensor, K., and Miranda, M. L. ``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.

Preprints:

1. Joseph Feldman and Daniel R. Kowal. “Bayesian quantile regression with subset selection: A posterior decision analysis perspective”. Major Revision at AOAS.

2. Joseph Feldman, Jerome P. Reiter, and Daniel R. Kowal. “Gaussian copula models for nonignorable missing data using auxiliary marginal quantiles”. Under review at JRSSB.

3. Joseph Feldman and Jerome P. Reiter. “Outcome-assisted multiple imputation of missing treatments”. Under review at Statistics in Medicine.

In Preparation:

1. Joseph Feldman and Jerome P. Reiter. “Nonparametric mean function estimation under differential privacy”

2. Joseph Feldman and Jerome P. Reiter. ``Differentially Private Synthetic Data Generation using Bayesian Gaussian Copulas"

Software

1. GMCImpute: Multiple imputation using the Gaussian mixture copula

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

3. QRSubsets: Subset selection for Bayesian quantile regression