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

Randall K. Kimmel, David E. Booth and Stephane Elise Booth

International Journal of Operational Research, 2010, vol. 7, issue 1, 1-15

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.

Keywords: early warning systems; locally weighted scatter plot smooth; LOESS; non-parametric; problem banks; robust regression; bank failures; local regression smoothing. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=29514 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:7:y:2010:i:1:p:1-15

Access Statistics for this article

More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().