Assessing influence on predictions from generalized linear models

William Thomas, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

Influence diagnostics for predictions from a normal linear model examine the effect of deleting a single case on either the point prediction or the predictive density function. Instead of deleting cases, we apply the local influence method of Cook (1986) to assess the effect of small perturbations of continuous data on a specified point prediction from a generalized linear model. Based on local perturbations of the vector of responses, case weights, explanatory variables, or the components of one case, the diagnostics can detect different kinds of influence. Some of the diagnostics are illustrated with an example and compared to standard diagnostic methods.

Original languageEnglish (US)
Pages (from-to)59-65
Number of pages7
JournalTechnometrics
Volume32
Issue number1
DOIs
StatePublished - Feb 1990

Bibliographical note

Funding Information:
Much of this article is taken from Thomas’s Ph.D. thesis, written under the supervision of Cook at the University of Minnesota. This work was supported in part by grants from the National Science Foundation.

Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.

Keywords

  • Data perturbation
  • Diagnostics
  • Leverage
  • Local influence

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