Linearly constrained estimation by mathematical programming

Linearly constrained estimation by mathematical programming

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Article ID: iaor1990341
Country: Netherlands
Volume: 42
Issue: 3
Start Page Number: 254
End Page Number: 267
Publication Date: Oct 1989
Journal: European Journal of Operational Research
Authors: , ,
Keywords: programming: geometric, programming: linear, programming: quadratic
Abstract:

Some mathematical programming models of the mixing problem are discussed in this paper. Five models, based on different discrepancies, are considered and their fundamental properties are examined. Using variational and Smirnov distances, linear programming models are obtained. Pearson divergence leads to quadratic programming. Hellinger divergence leads to lp-programming and the Kullback-Leibler information divergence gives a special geometric programming model. Finally some computational experiences are presented.

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