You are not currently logged in.
Access your personal account or get JSTOR access 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-The Missing Link
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 33, No. 1 (1984), pp. 18-24
Stable URL: http://www.jstor.org/stable/2347658
Page Count: 7
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
We consider generalized linear models, including an extension due to Thompson and Baker (1981), within the larger framework of multiparameter exponential family models. This general approach shows that the link function is not a necessary feature of a computer algorithm for calculating maximum likelihood estimates for such models by iteratively reweighted least squares. It is argued that the link function is a useful component of model fitting and interpretation in situations where there is a natural link to an underlying linear model (e.g. logistic regression). However in many instances there is no single link function (e.g., multinomial regression) or else unlinked parameters exist (e.g., bioassay with a spontaneous response rate). We attempt to show by a number of examples that a general approach via exponential family models is preferable in such situations.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1984 Royal Statistical Society