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Improved Added Variable and Partial Residual Plots for the Detection of Influential Observations in Generalized Linear Models

Author

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  • R.J. O'Hara Hines
  • E. M. Carter

Abstract

The added variable plots proposed by Pregibon and Wang and the partial residual plots proposed by Pregibon and Landwehr and co‐workers for generalized linear models have been found useful for examining the relationship between the dependent variable and an independent variable; however, they can give misleading impressions about influential observations. Alternative procedures in which the plots are based on the full rather than the reduced model, or on weighted rather than unweighted variables, prove to be more informative about such observations, giving information which is consistent with Pregibon's influence diagnostics in generalized linear models.

Suggested Citation

  • R.J. O'Hara Hines & E. M. Carter, 1993. "Improved Added Variable and Partial Residual Plots for the Detection of Influential Observations in Generalized Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 3-16, March.
    • Handle: RePEc:bla:jorssc:v:42:y:1993:i:1:p:3-16
    DOI: 10.2307/2347405
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    Cited by:

    1. Cai, Zongwu & Tsai, Chih-Ling, 1999. "Diagnostics for nonlinearity in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 29(4), pages 445-469, February.
    2. Guan-Hua Huang, 2005. "Selecting the number of classes under latent class regression: a factor analytic analogue," Psychometrika, Springer;The Psychometric Society, vol. 70(2), pages 325-345, June.
    3. Atkinson, A. C. & Koopman, S. J. & Shephard, N., 1997. "Detecting shocks: Outliers and breaks in time series," Journal of Econometrics, Elsevier, vol. 80(2), pages 387-422, October.

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