Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Multiple Deletions in Logistic Regression Models

  • Jung, Kang-Mo (Dept. of Informatics and Statistics, Kunsan National Univ.)
  • Published : 2009.03.30
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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.

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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 https://doi.org/10.2307/1268249
  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 https://doi.org/10.1093/biomet/34.3-4.320
  8. Jung, K.-M. (2007). Local influence of the quasi-likelihood estimators in generalized linear models, The Korean Coommunications in Statistics, 14, 229-239 https://doi.org/10.5351/CKSS.2007.14.1.229
  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 https://doi.org/10.2307/2344614
  11. Pregibon, D. (1981). Logistic regression diagnostics, The Annals of Statistics, 9,705-724 https://doi.org/10.1214/aos/1176345513
  12. Roy, S. S. and Guria, S. (2008). Diagnostics in logistic regression models, Journal of the Korean Statistical Society, 37, 89-94 https://doi.org/10.1016/j.jkss.2007.03.001
  13. Thomas, W. and Cook, R. D. (1989). Assessing influence on regression coefficients in generalized linear models, Biometrika, 76, 741-749 https://doi.org/10.1093/biomet/76.4.741