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A
Schifeling, T., C. Cheng, D. S. Hillygus, and J. P. Reiter. "Accounting for nonignorable unit nonresponse and attrition in panel studies with refreshment samples." Journal of Survey Statistics and Methodology 3, no. 3 (2015): 265-295. DOI: 10.1093/jssam/smv007, available at http://jssam.oxfordjournals.org/content/3/3/265.abstract.
Karr, A. F., and J. P. Reiter. "Analytical frameworks for data release: A statistical view." In Confidentiality and Data Access in the Use of Big Data: Theory and Practical Approaches. New York City, NY: Cambridge University Press, 2014.
Hu, J., R. Mitra, and J.P. Reiter. "Are independent parameter draws necessary for multiple imputation?" The American Statistician 67 (2013): 143-149. DOI: 10.1080/00031305.2013.821953, available at http://www.tandfonline.com/doi/full/10.1080/00031305.2013.821953.
McClure, D., and J. P. Reiter. "Assessing disclosure risks for synthetic data with arbitrary intruder knowledge." Statistical Journal of the International Association for Official Statistics 32, no. 1 (2016): 109-126. DOI: 10.3233/SJI-160957, available at http://content.iospress.com/download/statistical-journal-of-the-iaos/sji957.
B
Reiter, J. P., Q. Wang, and B. Zhang. "Bayesian estimation of disclosure risks for multiply imputed, synthetic data." Journal of Privacy and Confidentiality 6, no. 1 (2014), available at http://repository.cmu.edu/jpc/vol6/iss1/2.
Manrique-Vallier, D., and J.P. Reiter. "Bayesian estimation of discrete multivariate latent structure models with structural zeros." Journal of Computational and Graphical Statistics 23 (2014): 1061-1079.
Si, Yajuan, Jerome P. Reiter, and Sunshine D. Hillygus. "Bayesian Latent Pattern Mixture Models for Handling Attrition in Panel Studies With Refreshment Samples." ArXiv, no. 1509.02124 (2015), available at http://arxiv.org/abs/1509.02124.
Si, Y., J. P. Reiter, and D. S. Hillygus. "Bayesian latent pattern mixture models for handling attrition in panel studies with refreshment samples." Annals of Applied Statistics 10 (2016): 118-143. DOI: 10.1214/15-AOAS876, available at http://projecteuclid.org/euclid.aoas/1458909910.
Quick, H., S. H. Holan, C. K. Wikle, and J. P. Reiter. "Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography." ArXiv, no. 1407.7795 (2015), available at http://arxiv.org/abs/1407.7795.
Quick, Harrison, Scott H. Holan, Christopher K. Wikle, and Jerome P. Reiter. "Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography." Spatial Statistics 14 (2015): 439-451. DOI: 10.1016/j.spasta.2015.07.008, available at http://www.sciencedirect.com/science/article/pii/S2211675315000718.
DeYoreo, Maria, and Jerome P. Reiter. Bayesian mixture modeling for multivariate conditional distributions. ArXiv 1606.04457, 2016, available at http://arxiv.org/abs/1606.04457.
Manrique-Vallier, D., and J.P. Reiter. "Bayesian multiple imputation for large-scale categorical data with structural zeros." Survey Methodology 40 (2014): 125-134, available at http://www.stat.duke.edu/ jerry/Papers/SurvMeth14.pdf.
Manrique-Vallier, D., and J. P. Reiter. Bayesian multiple imputation for large-scale categorical data with structural zeros. Duke University / National Institute of Statistical Sciences (NISS) Preprint 1813:34889, 2013, available at http://hdl.handle.net/1813/34889.
Manrique-Vallier, Daniel, and Jerome P. Reiter. "Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data." Journal of the American Statistical Association (2016). DOI: 10.1080/01621459.2016.1231612, available at http://dx.doi.org/10.1080/01621459.2016.1231612.
C
Fosdick, B. K., Maria DeYoreo, and J. P. Reiter. Categorical data fusion using auxiliary information. arXiv 1506.05886, 2015, available at http://arxiv.org/abs/1506.05886.
Fosdick, B. K., M. De Yoreo, and J. P. Reiter. "Categorical data fusion using auxiliary information." Annals of Applied Statistics 10 (2016): 1907-1929. DOI: 10.1214/16-AOAS925, available at http://projecteuclid.org/euclid.aoas/1483606845.
D
Reiter, J. P., T. Schifeling, and M. De Yoreo. "Data fusion for correcting measurement errors." (Submitted).
Rose, A.. Data Fusion Methods for Improved Demographic Resolution of Population Distribution Datasets (Ph.D. Thesis). University of Tennessee phd, 2014.
Xiaolin, Yang, Stephen E. Fienberg, and Alessandro Rinaldo. "Differential Privacy for Protecting Multi-dimensional Contingency Table Data: Extensions and Applications." Journal of Privacy and Confidentiality 4 (2012): 101-125.
Chen, Y., A. Machanavajjhala, J. P. Reiter, and A. Barrientos. "Differentially private regression diagnostics." In IEEE International Conference on Data Mining., 2017.
Reiter, Jerry. Differentially Private Verification of Regression Model Results. NCRN Coordinating Office Preprint 1813:52167, 2016, available at http://hdl.handle.net/1813/52167.
Hu, Jingchen, Jerome P. Reiter, and Quanli Wang. "Dirichlet Process Mixture Models for Modeling and Generating Synthetic Versions of Nested Categorical Data." Bayesian Analysis (2017). DOI: 10.1214/16-BA1047, available at http://projecteuclid.org/euclid.ba/1485227030.
Hu, J., J.P. Reiter, and Q. Wang. "Dirichlet Process Mixture Models for Nested Categorical Data." ArXiv, no. 1412.2282 (2015), available at http://arxiv.org/pdf/1412.2282v3.pdf.
Hu, J., J.P. Reiter, and Q. Wang. "Disclosure risk evaluation for fully synthetic data." In Privacy in Statistical Databases, 185-199. Vol. 8744. Heidelberg: Springer, 2014.
Wang, Mengyang, Leah Ruppanner, and Allan L. McCutcheon. "Do ‘Don’t Know’ Responses = Survey Satisficing? Evidence from the Gallup Panel Paradata." In American Association for Public Opinion Research 2013 Annual Conference. Boston, MA, 2013, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.

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