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Metric Divergence Measures and Information Value in Credit Scoring

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  • Guoping Zeng

Abstract

Recently, a series of divergence measures have emerged from information theory and statistics and numerous inequalities have been established among them. However, none of them are a metric in topology. In this paper, we propose a class of metric divergence measures, namely, , and study their mathematical properties. We then study an important divergence measure widely used in credit scoring, called information value. In particular, we explore the mathematical reasoning of weight of evidence and suggest a better alternative to weight of evidence. Finally, we propose using as alternatives to information value to overcome its disadvantages.

Suggested Citation

  • Guoping Zeng, 2013. "Metric Divergence Measures and Information Value in Credit Scoring," Journal of Mathematics, Hindawi, vol. 2013, pages 1-10, October.
    • Handle: RePEc:hin:jjmath:848271
    DOI: 10.1155/2013/848271
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    References listed on IDEAS

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    1. Burbea, Jacob & Rao, C. Radhakrishna, 1982. "Entropy differential metric, distance and divergence measures in probability spaces: A unified approach," Journal of Multivariate Analysis, Elsevier, vol. 12(4), pages 575-596, December.
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    Cited by:

    1. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.

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