You are not currently logged in.
Access JSTOR through your library or other institution:
If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
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
Stable URL: http://www.jstor.org/stable/2344614
Page Count: 15
You can always find the topics here!Topics: Generalized linear model, Statistical models, Modeling, Statistical variance, Degrees of freedom, Linear models, Analysis of variance, Maximum likelihood estimation, Statism, Binomials
Were these topics helpful?See something inaccurate? Let us know!
Select the topics that are inaccurate.
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Preview not available
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.
Journal of the Royal Statistical Society. Series A (General) © 1972 Royal Statistical Society