Skip to main content

Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach

Buy Article:

$43.00 plus tax (Refund Policy)


We introduce a new class of generalized linear mixed models based on the Tweedie exponential dispersion model distributions, accommodating a wide range of discrete, continuous and mixed data. Using the best linear unbiased predictor of random effects, we obtain an optimal estimating function for the regression parameters in the sense of Godambe, allowing an efficient common fitting algorithm for the whole class. Although allowing full parametric inference, our main results depend only on the first- and second-moment assumptions of unobserved random effects. In addition, we obtain consistent estimators for both regression and dispersion parameters. We illustrate the method by analysing the epilepsy data and cake baking data. Along with simulations and asymptotic justifications, this shows the usefulness of the method for analysis of clustered non-normal data.
No References
No Citations
No Supplementary Data
No Article Media
No Metrics

Keywords: EM algorithm; Estimating equation; Generalized linear model; Random effects; Tweedie exponential dispersion model

Document Type: Research Article

Affiliations: 1: University of New Brunswick, Fredericton, Canada 2: University of Southern Denmark, Odense, Denmark

Publication date: 2007-09-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more