Using Global Optimization to Estimate Population Class Sizes

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...

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Bibliographic Details
Published in:Journal of global optimization 2006-11, Vol.36 (3), p.319-338
Main Authors: Greenberg, Betsy S, Lasdon, Leon S
Format: Article
Language:English
Subjects:
Class size
Confidentiality
Dentists
Economists
Estimating techniques
Nonlinear programming
Operations research
Optimization algorithms
Population
Studies
Citations: Items that this one cites
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Summary: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/NLP (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. [PUBLICATION ABSTRACT]
ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-006-9011-6