# Generalized Linear Models

**John A. Nelder****R. Baker**

CiteWeb id: 20060000009

CiteWeb score: 10709

DOI: 10.1002/0471667196.ess0866

SUMMARY The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log-likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components). The implications of the approach in designing statistics courses are discussed.

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John A. Nelder, R. Baker,

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