The multiple sets of deletion measures and masking in regression

Wang, D, Critchley, F and Smith, P 2003, 'The multiple sets of deletion measures and masking in regression', Communications in Statistics: Theory and Methods, vol. 32, no. 2, pp. 407-413.


Document type: Journal Article
Collection: Journal Articles

Title The multiple sets of deletion measures and masking in regression
Author(s) Wang, D
Critchley, F
Smith, P
Year 2003
Journal name Communications in Statistics: Theory and Methods
Volume number 32
Issue number 2
Start page 407
End page 413
Total pages 7
Publisher Marcel Dekker
Abstract Single set deletion influence analysis was introduced by Cook and Weisburg (Cook, R. D., Weisberg, S. (1982). Residuals and Influence in Regression. Chapman and Hall: New York); Pena and Yohai (Pena, D., Yohai, V. J. (1995). The detection of influential subsets in linear regression by using an influence matrix. J. R. Statist. Soc. B 57(1): 145-156); Barrett and Ling (Barrett, B. E., Ling, R. F. (1992). General classes of influence measures for multivariate regression. J. Amer. Stiatist. Assoc. 87(417):184-191); and Cook (Cook, R. D. (2000). Detection of influential observations inlinear regression. Technometrics 42(1):65-68). Multiple sets of deletion measures and conditional influence analysis are given in regression models by Wang and Critchley (Wang, D. Q., Critchley, F. (2000). Multiple deletion measures and influence in regression model. Communication in Statistics: Theory andMethods 29(11): 2391-2404). The present paper is concerned with multiple sets of deletion measures and masking problems in linear regression models. Masking problems have been discussed by Atkinson (Atkinson, A. C. (1985). Plot, Transformations and Regression. Clarendon: Oxford) and Lawrance (Lawrance, A. J. (1995). Deletion influence and masking in regression. J. R. Statist. Soc. B 57(1):181-189). In this paper we are particularly interested in conditional influence and masking problems for the detection of two influential subsets.
DOI - identifier 10.1081/STA-120018192
ISSN 0361-0926
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