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Multiple Deletions in Logistic Regression Models
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 Title & Authors
Multiple Deletions in Logistic Regression Models
Jung, Kang-Mo;
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 Abstract
We extended the results of Roy and Guria (2008) to multiple deletions in logistic regression models. Since single deletions may not exactly detect outliers or influential observations due to swamping effects and masking effects, it needs multiple deletions. We developed conditional deletion diagnostics which are designed to overcome problems of masking effects. We derived the closed forms for several statistics in logistic regression models. They give useful diagnostics on the statistics.
 Keywords
Conditional deletions;logistic regression models;masking effects;multiple deletions;outliers;swamping effects;
 Language
English
 Cited by
 References
1.
Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics, John Wiley & Sons, New York

2.
Cook, R. D. (1977). Detection of influential observations in linear regression, Technometrics, 19, 15-18 crossref(new window)

3.
Cook, R. D. (1986). Assessment of local influence, Journal of the Royal Statistical Society, Series B, 48, 133-169

4.
Cook, R. D. and Weisberg, S. (1982). Residuals and Influence in Regression, Chapman & Hall/CRC, London

5.
Dobson, A. J. (2002). An Introduction to Generalized Linear Models, 2nd Ed., Chapman & Hall/CRC, London

6.
Faraway, J. J. (2006). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC, London

7.
Finney, D. J. (1947). The estimation from individual records of the relationship between dose and quantal response, Biometrika, 34, 320-334 crossref(new window)

8.
Jung, K.-M. (2007). Local influence of the quasi-likelihood estimators in generalized linear models, The Korean Coommunications in Statistics, 14, 229-239 crossref(new window)

9.
Lawrance, A. J. (1995). Deletion influence and masking in regression, Journal of the Royal Statistical Society, Series B, 57, 181-189

10.
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models, Journal of the Royal Statistical Society, Series A, 135, 370-384 crossref(new window)

11.
Pregibon, D. (1981). Logistic regression diagnostics, The Annals of Statistics, 9,705-724 crossref(new window)

12.
Roy, S. S. and Guria, S. (2008). Diagnostics in logistic regression models, Journal of the Korean Statistical Society, 37, 89-94 crossref(new window)

13.
Thomas, W. and Cook, R. D. (1989). Assessing influence on regression coefficients in generalized linear models, Biometrika, 76, 741-749 crossref(new window)