Re: Generalized linear models

From: Murray Jorgensen <>
Date: Wed, 18 Feb 2009 17:38:13 +1300


if I understand your question you really seem to be thinking in terms of
transformations rather than glm-like linked models. In that case you
have the Box-Cox approach and you can estimate a transformation
parameter. In glms there is no simple characterisation of how the
systematic and random parts of the model combine to give you the data
(other than the definition of the glm, of course).


Patrick Cordue wrote:
> Hi All,
> For any given model it is usually straightforward to check if it satisfies
> the assumption of a GLM (and if it does, one can use glm() in R, for
> example, with an appropriate distribution family and link function, to
> obtain estimates of the coefficients, etc).
> However, given a dataset, which is analyzed using glm(), one may arrive at a
> "best model" which uses a particular family and link function (and a set of
> explanatory variables). The model is clearly described in terms of variables
> and structure for the mean response, and the distribution of the response
> variables is explicit, but the "exact" form of the error structure is not
> explicit (e.g., are the errors additive or multiplicative?, e.g., Y = a + bx
> + e, or Y = (a + bx) * e, both can have E(Y) = a + bx). I assume that some
> general results are available - can anyone point me to, for example, an
> online list of implicit error structure given the currently implemented
> families and link functions in R. Any comments, links, or references
> appreciated. TIA.
> --
> -----
> Patrick Cordue
> Director
> Innovative Solutions Ltd
> ----

Dr Murray Jorgensen
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email:      Fax 7 838 4155
Phone  +64 7 838 4773 wk    Home +64 7 825 0441   Mobile 021 0200 8350
Received on Wed Feb 18 2009 - 14:38:17 EST

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