The NSF-Census Research Network currently consists of eight nodes, each comprised of researchers conducting innovative, high-disciplinary investigations of theory, methodology and computational tools of interest and significance to the Census Bureau, the federal statistics system and the broader research community.

The eight nodes include Carnegie Mellon University, the University of Colorado at Boulder/University of Tennessee, Cornell University, Duke University/ National Institute of Statistical Sciences (NISS), the University of Michigan, the University of Missouri, the University of Nebraska and Northwestern University.

For more information, consult www.ncrn.info and http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503587

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Recent Submissions

  • NCRN Newsletter: Volume 2 - Issue 4 

    Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie (2016-01-28)
    Overview of activities at NSF-Census Research Network nodes from September 2015 through December 2015.
  • NCRN Newsletter: Volume 2 - Issue 3 

    Vilhuber, Lars; Karr, Alan; Reiter, Jerome; Abowd, John; Nunnelly, Jamie (2015-09-15)
    Overview of activities at NSF-Census Research Network nodes from June 2015 through August 2015.
  • Synthetic Establishment Microdata Around the World 

    Vilhuber, Lars; Abowd, John A.; Reiter, Jerome P. (Statistical Journal of the International Association for Official Statistics, 2016)
    In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large ...
  • Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics 

    Vilhuber, Lars; Miranda, Javier (Statistical Journal of the International Association for Official Statistics, 2016)
    We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive ...
  • Noise Infusion as a Confidentiality Protection Measure for Graph-Based Statistics 

    Abowd, John A.; McKinney, Kevin L. (Statistical Journal of the International Association for Official Statistics, 2016)
    We use the bipartite graph representation of longitudinally linked employer-employee data, and the associated projections onto the employer and employee nodes, respectively, to characterize the set of potential statistical ...

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