THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM‐BANK IDENTIFICATION MODEL*
作者:
David E. Booth,
期刊:
Decision Sciences
(WILEY Available online 1982)
卷期:
Volume 13,
issue 1
页码: 71-81
ISSN:0011-7315
年代: 1982
DOI:10.1111/j.1540-5915.1982.tb00130.x
出版商: Blackwell Publishing Ltd
关键词: Banking and Finance and Statistical Techniques
数据来源: WILEY
摘要:
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] thatM‐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 theM‐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
点击下载:
PDF
(488KB)
返 回