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A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model

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  • Ramos, Héctor M.
  • Ollero, Jorge
  • Suárez-Llorens, Alfonso

Abstract

We propose a new explanatory index for evaluating the binary logistic regression model based on the sensitivity of the estimated model. We previously formalized the idea of sensitivity and established the principles a statistic should comply with to be considered a sensitivity index. We apply the results to a practical example and compare the results with those obtained utilizing other indices.

Suggested Citation

  • Ramos, Héctor M. & Ollero, Jorge & Suárez-Llorens, Alfonso, 2017. "A new explanatory index for evaluating the binary logistic regression based on the sensitivity of the estimated model," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 135-140.
    • Handle: RePEc:eee:stapro:v:120:y:2017:i:c:p:135-140
    DOI: 10.1016/j.spl.2016.08.022
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    References listed on IDEAS

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    3. John G. Cragg & Russell S. Uhler, 1970. "The Demand for Automobiles," Canadian Journal of Economics, Canadian Economics Association, vol. 3(3), pages 386-406, August.
    4. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
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    2. Huasheng Zhu & Yawei Chen & Kebi Chen, 2019. "Vitalizing Rural Communities: China’s Rural Entrepreneurial Activities from Perspective of Mixed Embeddedness," Sustainability, MDPI, vol. 11(6), pages 1-21, March.

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