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THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM‐BANK IDENTIFICATION MODEL *
Author(s) -
Booth David E.
Publication year - 1982
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1982.tb00130.x
Subject(s) - mahalanobis distance , outlier , principal component analysis , multivariate statistics , estimator , identification (biology) , robust regression , computer science , robust statistics , regression analysis , regression , statistics , econometrics , mathematics , artificial intelligence , botany , biology
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.