Methodological framework demonstrates generalized linear models across diverse scientific disciplines, highlighting expanded applications for non-standard data distributions.
Addresses a class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis. Incorporates numerous exercises, both theoretical and data-analytic Discusses quasi-likelihood functions and estimating equations, models for dispersion effect, components of dispersion, and conditional likelihoods Holds particular interest for statisticians in medicine, biology, agriculture, social science, and engineering
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Therneau et al. (1993) studied this question.
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