@article {2040, title = {Dirichlet Process Mixture Models for Nested Categorical Data}, journal = {ArXiv}, year = {2015}, abstract = {We present a Bayesian model for estimating the joint distribution of multivariate categorical data when units are nested within groups. Such data arise frequently in social science settings, for example, people living in households. The model assumes that (i) each group is a member of a group-level latent class, and (ii) each unit is a member of a unit-level latent class nested within its group-level latent class. This structure allows the model to capture dependence among units in the same group. It also facilitates simultaneous modeling of variables at both group and unit levels. We develop a version of the model that assigns zero probability to groups and units with physically impossible combinations of variables. We apply the model to estimate multivariate relationships in a subset of the American Community Survey. Using the estimated model, we generate synthetic household data that could be disseminated as redacted public use files with high analytic validity and low disclosure risks. Supplementary materials for this article are available online.}, url = {http://arxiv.org/pdf/1412.2282v3.pdf}, author = {Hu, J. and Reiter, J.P. and Wang, Q.} } @article {handle:1813:40181, title = {NCRN Meeting Spring 2015: A Vision for the Future of Data Access}, number = {1813:40181}, year = {2015}, publisher = {NCRN Coordinating Office}, type = {Preprint}, abstract = {

NCRN Meeting Spring 2015: A Vision for the Future of Data Access Reiter, J.P. Presentation at the NCRN Meeting Spring 2015

}, url = {http://hdl.handle.net/1813/40181}, author = {Reiter, J.P.} } @article {1596, title = {Statistical Disclosure Limitation in the Presence of Edit Rules}, journal = {Journal of Official Statistics}, volume = {31}, year = {2015}, pages = {121-138}, chapter = {121}, author = {Kim, H.J. and Karr, A.F. and Reiter, J.P.} } @article {ManriqueReiter2013, title = {Bayesian estimation of discrete multivariate latent structure models with structural zeros}, journal = {Journal of Computational and Graphical Statistics}, volume = {23}, year = {2014}, pages = {1061-1079}, author = {Manrique-Vallier, D. and Reiter, J.P.} } @article {Hu13, title = {Are independent parameter draws necessary for multiple imputation?}, journal = {The American Statistician}, volume = {67}, year = {2013}, pages = {143-149}, doi = {10.1080/00031305.2013.821953}, url = {http://www.tandfonline.com/doi/full/10.1080/00031305.2013.821953}, author = {Hu, J. and Mitra, R. and Reiter, J.P.} } @article {Si2013, title = {Nonparametric Bayesian multiple imputation for incomplete categorical variables in large-scale assessment surveys}, journal = {Journal of Educational and Behavioral Statistics}, volume = {38}, year = {2013}, pages = {499-521}, url = {http://www.stat.duke.edu/~jerry/Papers/StatinMed14.pdf}, author = {Si, Y. and Reiter, J.P.} } @article {Manrique-Vallier2012, title = {Estimating identification disclosure risk using mixed membership models}, journal = {Journal of the American Statistical Association}, volume = {107}, year = {2012}, pages = {1385-1394}, author = {Manrique-Vallier, D. and Reiter, J.P.} } @article {Reiter2012, title = {Inferentially valid partially synthetic data: Generating from posterior predictive distributions not necessary}, journal = {Journal of Official Statistics}, volume = {28}, year = {2012}, pages = {583-590}, author = {Reiter, J.P. and Kinney, S.K.} }