General Approaches to Stepwise Identification of Unusual Values in Data Analysis

作者: Jeffrey S. Simonoff

DOI: 10.1007/978-1-4612-4444-8_13

关键词:

摘要: One of the general goals in data analysis is identification unusual values. This can be done indirectly (after performing a robust analysis) or directly (via some detection procedure). paper summarizes backwards-stepping approach to values set. has advantages simplicity application, flexibility, and resistance masking effects. Application univariate, multivariate, regression data, as well other problems, discussed. Simulations are used investigate properties this strategy for analysis. It shown that using appropriate procedures considerably more effective than indirect

参考文章(39)
M. B. Brown, Wilfrid Joseph Dixon, BMDP statistical software University of California Press. ,(1983)
F. J. Anscombe, Rejection of Outliers Technometrics. ,vol. 2, pp. 123- 146 ,(1960) , 10.1080/00401706.1960.10489888
Bernard Rosner, Percentage Points for a Generalized ESD Many-Outlier Procedure Technometrics. ,vol. 25, pp. 165- 172 ,(1983) , 10.1080/00401706.1983.10487848
William S. Krasker, Roy E. Welsch, Efficient Bounded-Influence Regression Estimation Journal of the American Statistical Association. ,vol. 77, pp. 595- 604 ,(1982) , 10.1080/01621459.1982.10477855
David C. Hoaglin, Boris Iglewicz, John W. Tukey, Performance of Some Resistant Rules for Outlier Labeling Journal of the American Statistical Association. ,vol. 81, pp. 991- 999 ,(1986) , 10.1080/01621459.1986.10478363
R. Dennis Cook, Detection of influential observation in linear regression Technometrics. ,vol. 42, pp. 65- 68 ,(2000) , 10.2307/1271434
Jeffrey S. Simonoff, Detecting outlying cells in two-way contingency table via backwards-stepping Technometrics. ,vol. 30, pp. 339- 345 ,(1988) , 10.2307/1270088
Bernard Rosner, On the Detection of Many Outliers Technometrics. ,vol. 17, pp. 221- 227 ,(1975) , 10.1080/00401706.1975.10489305
J. Brian Gray, Robert F. Ling, K-Clustering as a Detection Tool for Influential Subsets in Regression Technometrics. ,vol. 26, pp. 305- 318 ,(1984) , 10.1080/00401706.1984.10487980