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

From   Maarten Buis <>
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Tue, 18 Dec 2012 20:02:40 +0100

On Tue, Dec 18, 2012 at 7:24 PM, Laura R. wrote:
> I thought generalized linear models (this is what I meant with glm)
> support different distributions of the dependent variable y, not the
> residuals.

You are right, it is a model for the conditional distribution of y and
not the residuals. This means that the marginal distribution for the
dependent variable can deviate considerably from the unconditional
distribution that gave your model its name. See:
<>. This is not a
problem, but you do need to take that into account when you diagnose
your problem.

Only in special cases (e.g. the normal/Gaussian distribution) does the
distribution of the residuals correspond to the unconditional

> My dependent variable and the residuals are both right
> skewed, so maybe glm with inverse gaussian would be good.

Maybe, but I should not get carried away like that. I can very well
imagine that it is better in your case to stick to simple linear
regression with robust standard errors. I realise that this
contradicts some other advise you have been given. This is a sign that
you have come at a point where the decision has to be based on the
specifics of your study, which means that we can no longer help you.

Hope this helps,

Maarten L. Buis
Reichpietschufer 50
10785 Berlin
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