Analyzing repeated measures on generalized linear models via the bootstrap

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The analysis of longitudinal data for which the response variables have nonnormal error distributions previously has been complex and/or dependent on restrictive assumptions. In this paper single methods are introduced for the class of generalized linear models (GLMs). Regressions are fit to the data at each observation time; functions of the resulting coefficients may be bootstrapped, or the coefficients combined through closed-form estimation of their covariances. Application is made to a data set on xerophthalmia in Indonesian children.

Original languageEnglish (US)
Pages (from-to)381-394
Number of pages14
Issue number2
StatePublished - Jan 1 1989
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics


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