Using global optimization to estimate population class sizes

Using global optimization to estimate population class sizes

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Article ID: iaor20071526
Country: Netherlands
Volume: 36
Issue: 3
Start Page Number: 319
End Page Number: 338
Publication Date: Nov 2006
Journal: Journal of Global Optimization
Authors: ,
Keywords: programming: nonlinear
Abstract:

In this paper we formulate a nonlinear optimization model to estimate population class sizes based on sample information. The model 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/NPL (OQNLP). This can be used to estimate the number of individuals in a population with unique or rarely occurring characteristics, which is useful for assessing disclosure risk. It can also be used to estimate the number of classes in a population, a problem with applications in a variety of disciplines.

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