A Generalized Extreme Studentized Residual Multiple-Outlier-Detection Procedure in Linear Regression
作者:
S.R. Paul,
KarenY. Fung,
期刊:
Technometrics
(Taylor Available online 1991)
卷期:
Volume 33,
issue 3
页码: 339-348
ISSN:0040-1706
年代: 1991
DOI:10.1080/00401706.1991.10484839
出版商: Taylor & Francis Group
关键词: Maximum absolute studentized residual;Two-phase procedure
数据来源: Taylor
摘要:
This article is concerned with procedures for detecting multipleyouthers in linear regression. A generalized extreme studentized residual (GESR) procedure, which controls type I error rate, is developed. An approximate formula to calculate the percentiles is given for large samples and more accurate percentiles forn≤ 25 are tabulated. The performance of this procedure is compared with others by Monte Carlo techniques and found to be superior. The procedure. however, fails in detectingyoutliers that are on high-leverage cases. For this. a two-phase procedure is suggested. In phase 1, a set of suspect observations is identified by GESR and one of the diagnostics applied sequentially. In phase 2, a backward testing is conducted using the GESR procedure to see which of the suspect cases are outlicrs. Several examples are analyzed.
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