| Abstract: | Because the eight largest bank failures in United States history have occurred since 1973 24], the development of early-warning problem-bank identification models is an important undertaking. It has been shown previously 3] 5] that M-estimator robust regression provides such a model. The present paper develops a similar model for the multivariate case using both a robustified Mahalanobis distance analysis 21] and principal components analysis 10]. In addition to providing a successful presumptive problem-bank identification model, combining the use of the M-estimator robust regression procedure and the robust Mahalanobis distance procedure with principal components analysis is also demonstrated to be a general method of outlier detection. The results from using these procedures are compared to some previously suggested procedures, and general conclusions are drawn. |
