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An Application of Monte‐Carlo‐Based Sensitivity Analysis on the Overlap in Discriminant Analysis

Author

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  • S. Razmyan
  • F. Hosseinzadeh Lotfi

Abstract

Discriminant analysis (DA) is used for the measurement of estimates of a discriminant function by minimizing their group misclassifications to predict group membership of newly sampled data. A major source of misclassification in DA is due to the overlapping of groups. The uncertainty in the input variables and model parameters needs to be properly characterized in decision making. This study combines DEA‐DA with a sensitivity analysis approach to an assessment of the influence of banks’ variables on the overall variance in overlap in a DA in order to determine which variables are most significant. A Monte‐Carlo‐based sensitivity analysis is considered for computing the set of first‐order sensitivity indices of the variables to estimate the contribution of each uncertain variable. The results show that the uncertainties in the loans granted and different deposit variables are more significant than uncertainties in other banks’ variables in decision making.

Suggested Citation

  • S. Razmyan & F. Hosseinzadeh Lotfi, 2012. "An Application of Monte‐Carlo‐Based Sensitivity Analysis on the Overlap in Discriminant Analysis," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:315868
    DOI: 10.1155/2012/315868
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    References listed on IDEAS

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    Cited by:

    1. Xiaomei Hu & Zhifeng Xu & Hongxia Cai & Junjun Hu, 2014. "Kinetic‐Monte‐Carlo‐Based Parallel Evolution Simulation Algorithm of Dust Particles," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).

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