Abstract #301023

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JSM 2003 Abstract #301023
Activity Number: 285
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Government Statistics
Abstract - #301023
Title: Using Global Optimization to Estimate Disclosure Risk
Author(s): Betsy S. Greenberg*+ and Leon Lasdon
Companies: University of Texas and University of Texas
Address: CBA 5.202, 46500, Austin, TX, 78712,
Keywords: confidentiality ; disclosure risk ; global optimization ; number of species ; population uniques
Abstract:

We formulate an optimization program to estimate population class sizes based on sample information. The resulting nonlinear program is nonconvex and has several local minima corresponding to different populations that could have been the source of the sample data. We show that many if not all local solutions can be found using a new global optimization algorithm called OptQuest Multi-Start (OQMS). The results can be used to estimate the number of individuals in a population with unique characteristics, a quantity useful for assessing disclosure risk. The results can also be used to estimate the number of classes (or species) in a population.

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