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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   "Laura R." <laura.roh@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Tue, 18 Dec 2012 19:24:38 +0100

Thank you very much for your support.

I thought generalized linear models (this is what I meant with glm)
support different distributions of the dependent variable y, not the
residuals. My dependent variable and the residuals are both right
skewed, so maybe glm with inverse gaussian would be good.

Laura



2012/12/17 JVerkuilen (Gmail) <jvverkuilen@gmail.com>:
> On Mon, Dec 17, 2012 at 12:32 PM, Laura R. <laura.roh@googlemail.com> wrote:
>> Thank you all for your help. I am still a bit confused, because now I
>> read that also with GLM homoscedasticity and normality of residuals
>> are assumptions that have to be met. But I will research further on
>> that type of models in order to find out whether this works better in
>> my case than OLS.
>
> Yes, as I'm stuck teaching a course titled GLM which is for "general
> linear model" I always tell students that the terminology, like
> everything else in Grad Center, is out of date.
>
> A generalized linear model lets you switch the error distribution to
> something like the gamma or inverse Gaussian, which would more
> naturally accommodate the skewed errors. This puts the transformation
> on the regression structure, not the data themselves.
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