- Thread starter hlsmith
- Start date

https://www.groups.ma.tum.de/fileadmin/w00ccg/statistics/czado/lec8.pdf

Currently with the the logging, I can say for every blank days the outcome goes down blank percent and it looks like I just have a handful of unilateral wider points in the S / SW portion of the residual plots.

If the DV is continuous and strictly positive... maybe Gamma Regression?

The gamma distribution and the log-normaldistribution are similar, but one of them has a heavier tail (but I don't remember which one).

Or you can choose an other distribution for positive data - Weibull maybe?

gamlss has a lot of distributions to choose from.

If there are values "under the detection point", so that they are censored, Helsel has written about that.

Interesting stuff, any other suggestions (e.g., inverse gaussian). Below are stdize deviance residuals:

Gamma, log link

Normal, identity

Normal, identity, with logged data

The gamma looks like it wins. I may also look at fit statistics between models. Seems the gamma with log link is multiplicative in nature, so I believe I exp the coefficients and describe them as an estimated blank times change in mean.

Gamma, log link

Normal, identity

Normal, identity, with logged data

The gamma looks like it wins. I may also look at fit statistics between models. Seems the gamma with log link is multiplicative in nature, so I believe I exp the coefficients and describe them as an estimated blank times change in mean.

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I believe for me in this setting, where the DV is bounded by '0' (see first plot in first post), it matters since the residuals are heterogenous making the SEs off and if ones was to extrapolate outside the far right range things could be not as good as when the issue is addressed. For reference, my sample size is permanently fixed at 200.

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Below is a fitted linear reg (top) and gamma (w/log link; bottom). I believe the data generating function for those outliers in the top middle of plots are based on an additional latent exposure. I may try to address them in a sensitivity analysis. Any feedback or thoughts in general about these fits. I think my preference is still for the latter model/plot, since it is likely asymptotic to the zero boundedness.

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Of course both of those comments are assumption and not everyone would agree that White's is enough (I think with enough cases the consensus today would be normality is not a concern).