The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Logistic Regression Diagnostics
Open accessAbstract
A maximum likelihood fit of a logistic regression model (and other similar models) is extremely sensitive to outlying responses and extreme points in the design space. We develop diagnostic measures to aid the analyst in detecting such observations and in quantifying their effect on various aspects of the maximum likelihood fit. The elements of the fitting process which constitute the usual output (parameter estimates, standard errors, residuals, etc.) will be used for this purpose. With a properly designed computing package for fitting the usual maximum-likelihood model, the diagnostics are essentially "free for the asking." In particular, good data analysis for logistic regression models need not be expensive or time-consuming.
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Cites
Nelder, J. A., & Wedderburn, R. W. M. (1972) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344614 · Cited by 7,194
Regression Diagnostics -- Identifying Influential Data and Sources of Collinearity
Beaumont, C. D., Belsley, D. A., Kuh, E., & Welsch, R. E. (1981) · The Journal of the Operational Research Society · doi:10.2307/2581267 · Cited by 6,359
Detection of Influential Observation in Linear Regression
Cook, R. D. (2000) · Technometrics · doi:10.2307/1271434 · Cited by 2,269
Holder, R. L., Mosteller, F., & Tukey, J. W. (1979) · Applied Statistics · doi:10.2307/2346739 · Cited by 1,676
The Hat Matrix in Regression and ANOVA
Hoaglin, D. C., & Welsch, R. E. (1978) · The American Statistician · doi:10.1080/00031305.1978.10479237 · Cited by 912
Influential Observations in Linear Regression
Cook, R. D. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10481634 · Cited by 830
Goodness of Link Tests for Generalized Linear Models
Pregibon, D. (1980) · Applied Statistics · doi:10.2307/2346405 · Cited by 751
Self-citationFinding the Outliers that Matter
Andrews, D. F., & Pregibon, D. (1978) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1978.tb01652.x · Cited by 290
Self-citationWelsch, R. E., & Kuh, E. (1977) · National Bureau of Economic Research · doi:10.3386/w0173 · Cited by 156
‘Fitting nonlinear models: numerical techniques’
Chambers, J. M. (1973) · Biometrika · doi:10.1093/biomet/60.3.669 · Cited by 65
Cited by
Residuals and Influence in Regression.
Atkinson, A. C., Cook, R. D., & Weisberg, S. (1983) · Biometrics · doi:10.2307/2531125 · Cited by 1,904
Cook, R. D. (1986) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1986.tb01398.x · Cited by 1,044
Generalized Additive Models: Some Applications
Hastie, T., & Tibshirani, R. (1987) · Journal of the American Statistical Association · doi:10.1080/01621459.1987.10478440 · Cited by 870
Generalized modeling approaches to risk adjustment of skewed outcomes data
Manning, W. G., Basu, A., & Mullahy, J. (2005) · Journal of Health Economics · doi:10.1016/j.jhealeco.2004.09.011 · Cited by 760
Green, P. J. (1984) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1984.tb01288.x · Cited by 687
Metadata
- Authors
- Pregibon, D.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1981-07-01
- Volume
- 9
- Issue
- 4
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2020214980
- Cited by
- 1,290
- References
- 10
- FWCI
- 17.17
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
