Model-checking techniques for linear models with parametric variance functions

Patrick G. Arbogast, Edward J. Bedrick

Research output: Contribution to specialist publicationArticle

4 Scopus citations

Abstract

We present graphical and numerical methods for assessing the adequacy of the log-linear model for variances. The proposed methods are derived from the cumulative sum of residuals over the covariate or linear predictor. Under the assumed model, the cumulative residual process converges weakly to a 0-mean Gaussian process whose distribution can be approximated via Monte Carlo simulation. The observed cumulative residual pattern can then be compared both visually and analytically to a number of simulated realizations from the approximate null distribution. These comparisons enable one to examine the functional form of each covariate, the link function as well as the overall model adequacy of the variance model. Simulation studies demonstrate that the proposed methods perform well in practical settings. Illustrations with three datasets are provided.

Original languageEnglish (US)
Pages404-410
Number of pages7
Volume46
No4
Specialist publicationTechnometrics
DOIs
StatePublished - Nov 2004
Externally publishedYes

Keywords

  • Goodness of fit
  • Lack of fit
  • Model misspecification
  • Regression diagnostic
  • Residual
  • Variance heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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