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A Gras Variant Solving For Minimum Information Loss

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  • Andre Lemelin
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    Abstract

    The fundamental idea in Junius and Oosterhaven (2003) is to break down the information contained in the a priori data into two parts: algebraic signs, and absolute values. This approach is well grounded in information theory, and provides a basis on which to solve the problem of adjusting matrices with negative entries. However, Junius and Oosterhaven (2003) have formulated a target function that is not equivalent to the Kullback and Leibler (1951) cross-entropy measure, and so is not a representation of the minimum information loss principle. Neither is the alternative target function proposed by Lenzen et al. (2007). This paper develops the exact Kullback and Leibler cross-entropy measure. In addition, following the constrained optimization approach, this paper applies the same principle to solve adjustment problems where row-sums, column-sums or both are constrained to zero.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Economic Systems Research.

    Volume (Year): 21 (2009)
    Issue (Month): 4 ()
    Pages: 399-408

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    Handle: RePEc:taf:ecsysr:v:21:y:2009:i:4:p:399-408

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    Keywords: RAS matrix balancing; Negative entries; GRAS; Minimum cross-entropy;

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