Using global optimization to estimate population class sizes

作     者：Greenberg, Betsy S. Lasdon, Leon S. 

作者机构：Univ Texas McCombs Sch Business IROM Dept Austin TX 78712 USA 

出 版 物：《JOURNAL OF GLOBAL OPTIMIZATION》 (全局最优化杂志)

年 卷 期：2006年第36卷第3期

页      面：319-338页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：global optimization confidentiality disclosure risk number of species nonlinear programming microdata statistical estimation computational experiments 

摘      要：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.