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Generalized Linear Models

J. A. Nelder and R. W. M. Wedderburn
Journal of the Royal Statistical Society. Series A (General)
Vol. 135, No. 3 (1972), pp. 370-384
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2344614
Stable URL: http://www.jstor.org/stable/2344614
Page Count: 15
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Generalized Linear Models
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Abstract

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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