TY - JOUR T1 - Dirichlet Process Mixture Models for Nested Categorical Data JF - ArXiv Y1 - 2015 A1 - Hu, J. A1 - Reiter, J.P. A1 - Wang, Q. AB - 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. UR - http://arxiv.org/pdf/1412.2282v3.pdf IS - 1412.2282 ER - TY - JOUR T1 - Simultaneous Edit-Imputation for Continuous Microdata JF - Journal of the American Statistical Association Y1 - 2015 A1 - Kim, H. J. A1 - Cox, L. H. A1 - Karr, A. F. A1 - Reiter, J. P. A1 - Wang, Q. VL - 110 UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1040881 ER - TY - JOUR T1 - Bayesian estimation of disclosure risks for multiply imputed, synthetic data JF - Journal of Privacy and Confidentiality Y1 - 2014 A1 - Reiter, J. P. A1 - Wang, Q. A1 - Zhang, B. AB -

Agencies seeking to disseminate public use microdata, i.e., data on individual records, can replace confidential values with multiple draws from statistical models estimated with the collected data. We present a famework for evaluating disclosure risks inherent in releasing multiply-imputed, synthetic data. The basic idea is to mimic an intruder who computes posterior distributions of confidential values given the released synthetic data and prior knowledge. We illustrate the methodology with artificial fully synthetic data and with partial synthesis of the Survey of Youth in Custody.

VL - 6 UR - http://repository.cmu.edu/jpc/vol6/iss1/2 IS - 1 ER - TY - JOUR T1 - Multiple imputation of missing or faulty values under linear constraints JF - Journal of Business and Economic Statistics Y1 - 2014 A1 - Kim, H. J. A1 - Reiter, J. P. A1 - Wang, Q. A1 - Cox, L. H. A1 - Karr, A. F. AB -

Many statistical agencies, survey organizations, and research centers collect data that suffer from item nonresponse and erroneous or inconsistent values. These data may be required to satisfy linear constraints, for example, bounds on individual variables and inequalities for ratios or sums of variables. Often these constraints are designed to identify faulty values, which then are blanked and imputed. The data also may exhibit complex distributional features, including nonlinear relationships and highly nonnormal distributions. We present a fully Bayesian, joint model for modeling or imputing data with missing/blanked values under linear constraints that (i) automatically incorporates the constraints in inferences and imputations, and (ii) uses a flexible Dirichlet process mixture of multivariate normal distributions to reflect complex distributional features. Our strategy for estimation is to augment the observed data with draws from a hypothetical population in which the constraints are not present, thereby taking advantage of computationally expedient methods for fitting mixture models. Missing/blanked items are sampled from their posterior distribution using the Hit-and-Run sampler, which guarantees that all imputations satisfy the constraints. We illustrate the approach using manufacturing data from Colombia, examining the potential to preserve joint distributions and a regression from the plant productivity literature. Supplementary materials for this article are available online.

VL - 32 ER -