Deletion diagnostics for marginal mean and correlation model parameters in estimating equations

John S. Preisser, Jamie Perin

Research output: Contribution to journalArticlepeer-review

Abstract

Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations. Estimating equations for intracluster correlations facilitate computational formulae for one-step deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model. The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model. The diagnostics are applied to data from four research studies from public health and medicine.

Original languageEnglish (US)
Pages (from-to)381-393
Number of pages13
JournalStatistics and Computing
Volume17
Issue number4
DOIs
StatePublished - Dec 1 2007
Externally publishedYes

Keywords

  • Cluster trial
  • Generalized estimating equations
  • Influence
  • Intracluster correlation
  • Logit models

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics

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