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Generalizations of Logistic Regression, Weight of Evidence, and the Gini Index for a Continuous Target Variable Taking on Probabilistic Values

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Abstract

The author proposes original tools for improving formulas of the maximum likelihood estimation method for logistic regression, the weight of evidence formula including the information value indicator formula, and the Gini index with a view to providing the use of a continuous target variable assuming probabilistic values. The approach to the pursuance of this research consists of the application of continuous weight functions meeting certain conditions to evaluate a generalized logarithm of the likelihood function including its generalized gradient vector and generalized Hessian matrix and also the application of probability theory to generalize weight of evidence and the Gini index.

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References

  1. A. A. Lobanova and A. V. Chugunova, Encyclopedia of Financial Risk Management [in Russian], Alpina Publisher, Moscow (2003).

    Google Scholar 

  2. Siddiqi Naeem, Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring, Wiley, Hoboken (2006).

    Google Scholar 

  3. Lyn C. Thomas, David B. Edelman, and Jonathan N. Crook, Credit Scoring and Its Applications, Society for Industrial and Applied Mathematics, Philadelphia (2002).

    Book  MATH  Google Scholar 

  4. Paul D. Allison, Logistic Regression Using the SAS® System: Theory and Application, SAS Institute Inc., Cary (1999).

    Google Scholar 

  5. E. Mays, Handbook of Credit Scoring, Fitzroy Dearborn, Chicago (2001).

    Google Scholar 

  6. O. M. Soloshenko, “Improvement of the iterative classification method for including rejected applications into credit scoring,” Science News of NTUU “KPI,” No. 5, 63–69 (2014).

  7. O. M. Soloshenko, “A method for computing the Gini index, Kolmogorov–Smirnov statistics, and Makhalanobis distance in credit scoring using SQL language tools,” Science News of NTUU “KPI,” No. 1, 29–35 (2015).

  8. A. N. Terent’yev and P. I. Bidyuk, “Method of probabilistic inference from learning data in Bayesian networks,” Cybernetics and Systems Analysis, 43, No. 3, 391–396 (2007).

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, Ukraine

    O. M. Soloshenko

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  1. O. M. Soloshenko
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Correspondence to O. M. Soloshenko.

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Translated from Kibernetika i Sistemnyi Analiz, No. 6, November–December, 2015, pp. 174–187.

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Soloshenko, O.M. Generalizations of Logistic Regression, Weight of Evidence, and the Gini Index for a Continuous Target Variable Taking on Probabilistic Values. Cybern Syst Anal 51, 992–1004 (2015). https://doi.org/10.1007/s10559-015-9792-z

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  • DOI: https://doi.org/10.1007/s10559-015-9792-z

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