Linearly constrained estimation by mathematical programming

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 l p -programming and the Kullback-Leibler information divergence gives a special geometric programming model. Finally some computational experiences are presented.

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