Abstract
ABSTRACTBecause 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.
Published Version
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