Diagnostics for mixed – model analysis of variance

Richard J. Beckman, Christopher J. Nachtsheim, R. Dennis Cook

Research output: Contribution to journalArticlepeer-review

140 Scopus citations


We describe a new method for assessment of model inadequacy in maximum-likelihood mixed-model analysis of variance. In particular, we discuss its use in diagnosing perturbations from the usual assumption of constant error variance and from the assumption that each realization of a given random factor has been drawn from the same normal population. Computer implementation of the procedure is described, and an example is presented, involving the analysis of filter cartridges used with commercial respirators.

Original languageEnglish (US)
Pages (from-to)413-426
Number of pages14
Issue number4
StatePublished - Nov 1987

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation Grant DMS-860 1314 and by Nuclear Regulatory Commission Grant RES 60-83-176. We would like to thank Mark Johnson and Luis Escobar for their comments on an earlier draft of this article. We also thank A. Juan.


  • Curvature
  • Likelihood displacement
  • Local influence
  • Random effects


Dive into the research topics of 'Diagnostics for mixed – model analysis of variance'. Together they form a unique fingerprint.

Cite this