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The analysis of outlying data points by robust Locally Weighted Scatter Plot Smooth: a model for the identification of problem banks

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  • Randall K. Kimmel
  • David E. Booth
  • Stephane Elise Booth

Abstract

Continuing bank failures point to the need for early warning problem bank identification models to guide the actions of regulators and investors. Several models have been shown to work well, but most require extensive data preparation/manipulation and custom computer programmes to analyse the data, which hinders widespread adoption. In this article, we show that robust Locally Weighted Scatter Plot Smooth, a type of Local Regression Smoothing, which requires minimal data preparation and can be run in many off the shelf statistical packages such as SAS and SPSS, can be just as effective as an early warning system.

Suggested Citation

  • Randall K. Kimmel & David E. Booth & Stephane Elise Booth, 2010. "The analysis of outlying data points by robust Locally Weighted Scatter Plot Smooth: a model for the identification of problem banks," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 7(1), pages 1-15.
    • Handle: RePEc:ids:ijores:v:7:y:2010:i:1:p:1-15
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    Cited by:

    1. Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.

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