THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM‐BANK IDENTIFICATION MODEL*
针对1973年以来美国八次最大银行倒闭事件，开发了基于M估计稳健回归和稳健马氏距离的多变量问题银行早期预警模型，并展示了该方法作为通用异常值检测工具的有效性。
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.