1 citations · 2 across the 4 of their papers we have counts for
4 papers
Gaussian Copula Models for Nonignorable Missing Data Using Auxiliary Marginal Quantiles
Joseph Feldman, Jerome P. Reiter, Daniel R. Kowal
We present an approach for modeling and imputation of nonignorable missing data. Our approach uses Bayesian data integration to combine (1) a Gaussian copula model for all study va…
Bayesian Quantile Regression with Subset Selection: A Decision Analysis Perspective
Joseph Feldman, Daniel Kowal
Quantile regression is a powerful tool for inferring how covariates affect specific percentiles of the response distribution. Existing methods either estimate conditional quantiles…
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data
Joseph Feldman, Daniel R. Kowal
Modern datasets commonly feature both substantial missingness and many variables of mixed data types, which present significant challenges for estimation and inference. Complete ca…
Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data
Joseph Feldman, Daniel Kowal
Much of the micro data used for epidemiological studies contain sensitive measurements on real individuals. As a result, such micro data cannot be published out of privacy concerns…