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Influence Contours in Linear Regression

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Summary

A graphical method to study influence measures in linear regression is proposed. The approach is based on adding a new observation to an existing data set. An influence contour is defined as the solution to

$${\rm{Influence (new observation) }} = {\rm{ Constant}}{\rm{. }}$$

Influence contours are derived and discussed for Cook’s Distance, DFFITS, DFBETAS, and R. Example contour plots are given for each measure. Influence contours are shown to be a useful tool for understanding and comparing the regions of influence for the various influence measures.

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Authors and Affiliations

  1. Department of Computer Science, University of South Carolina, Columbia, SC, 29208, USA

    Zsolt Lengvárszky

  2. Department of Statistics, University of South Carolina, Columbia, SC, 29208, USA

    R. Webster West

Authors
  1. Zsolt Lengvárszky
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  2. R. Webster West
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Lengvárszky, Z., West, R.W. Influence Contours in Linear Regression. Computational Statistics 17, 465–477 (2002). https://doi.org/10.1007/s001800200120

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