JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 346
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #312390
Title: Nested Dirichlet Process Model for Household Data Set Synthesis
Author(s): Jingchen Hu*+ and Jerome P. Reiter
Companies: Duke University and Duke University
Keywords: synthetic datasets ; latent class model ; disclosure risk

This project is focused on generating partial synthetic datasets for households, with the application for decennial census household synthesis. Extensions of nested Dirichlet Process model are developed to allow two-level clustering of households and individuals in households. Both household-level variables and individual-level variables can be modeled, and the model provides good data utility in terms of recovering the marginal, bivariate distributions of the variables in the original dataset, as well as within household structures. A data augmentation method to account for the relational structural zeros in a household dataset is developed. Risk measure computation methods based on computing the posterior probability of one record being identified given the synthetic datasets and other information available to the intruder, are developed.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program

2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.