Jerome P. Reiter
Professor of Statistical Science
My primary research focus has been investigating statistical methods of preserving data confidentiality. More generally, I am interested in the analysis of complex surveys, especially missing data methods and causal inference. I enjoy collaborating on data analyses with researchers who are not statisticians, particularly in the social sciences. I also am the associate director of the Information Initiative at Duke (iiD).
Addressing Bias from Missing Data in EHR Based Studies of CVD awarded by National Institutes of Health (Collaborator). 2018 to 2023
Integrating Multiple Data Sources to Account for Nonignorable Nonresponse awarded by National Science Foundation (Principal Investigator). 2017 to 2019
CIF21 DIBBs: An Integrated System for Public/Private Access to Large-scale, Confidential Social Science Data awarded by National Science Foundation (Principal Investigator). 2015 to 2018
Computatopmal Methods and Tools for Creating High Quality, Public Use Census Microdata awarded by Alfred P. Sloan Foundation (Principal Investigator). 2016 to 2017
NCRN-MN:Triangle Census Research Network awarded by National Science Foundation (Principal Investigator). 2011 to 2016
TC:Large: Collaborative Research: Practical Privacy: Metrics and Methods for Protecting Record-level and Relational Data awarded by National Science Foundation (Principal Investigator). 2010 to 2016
Multiple Imputation Methods for Handling Missing Data in Longitudinal Studies with Refreshment Samples awarded by National Science Foundation (Principal Investigator). 2011 to 2014
Sharing Confidential Datasets With Geographic Identifiers Via Multiple Imputation awarded by National Institutes of Health (Principal Investigator). 2009 to 2012
MMS: Methodology for Improving Public Use Data Dissemination Via Multiply-Imputer, Partially Synthetic Data awarded by National Science Foundation (Principal Investigator). 2008 to 2011
Yu, H, and Reiter, JP. "Differentially private verification of regression predictions from synthetic data." Transactions on Data Privacy 11.3 (December 1, 2018): 279-297.
Dalzell, NM, and Reiter, JP. "Regression Modeling and File Matching Using Possibly Erroneous Matching Variables." Journal of Computational and Graphical Statistics 27.4 (October 2, 2018): 728-738. Full Text
Sadinle, M, and Reiter, JP. "Sequential identification of nonignorable missing data mechanisms." Statistica Sinica 28.4 (October 1, 2018): 1741-1759. Full Text
Wortman, JH, and Reiter, JP. "Simultaneous record linkage and causal inference with propensity score subclassification." Statistics in Medicine 37.24 (October 2018): 3533-3546. Full Text
Lott, A, and Reiter, JP. "Wilson Confidence Intervals for Binomial Proportions With Multiple Imputation for Missing Data(Accepted)." American Statistician (January 1, 2018). Full Text
Paiva, T, and Reiter, J. "Using Imputation Techniques to Evaluate Stopping Rules in Adaptive Survey Designs." (September 1, 2014).
Kinney, SK, Reiter, J, and Miranda, J. "Improving the Synthetic Longitudinal Business Database." (February 1, 2014).
Reiter, JP. "Research synthesis: Statistical approaches to protecting confidentiality for microdata and their effects on the quality of statistical inferences." Public Opinion Quarterly 76.1 (March 1, 2012): 163-181. Full Text
White, K, Reiter, J, and Petrin, A. "Plant-Level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data." (February 2012).