I think that generalized linear models with appropriate error distributions &

link functions can often yield results that are more interpretable. (I

think this is what Rich was getting at when he mentioned "non-linear maximum

likelihood models".) Here's an example for the case where the outcome

variable is positive and positively skewed:

http://rstudio-pubs-static.s3.amazonaws.com/5691_192685385fc445c9b3fb1619960a20e2.htmlNotice especially the Differences and Similarities section, where the author

says this:

"Thus, if the outcome is log transformed before entering the linear

regression model, the inference about the geometric mean. In contrast, the

generalized linear model approach allows inference about the arithmetic mean

on the original scale."

Finally, the models estimated on that page using R can also be estimated

using GENLIN, as it allows one to select a Gamma error distribution.

https://www.ibm.com/support/knowledgecenter/en/SSLVMB_25.0.0/statistics_reference_project_ddita/spss/advanced/syn_genlin_model.htmlHTH.

fjmenendez wrote

> Thanks Art, thanks Rich for your kindness and your knowledge :)

>

> I read the posts about outliers in the list, and I have benefited from

> them. The idea of thinking about them as suspicious values that need

> additional checking before decision makes a lot of sense for me. Perhaps I

> should think this topic using different words.

> I don't know the anomalous values tool more than superficially. Perhaps it

> is a good idea reread about it.

>

> Also transformations deserve attention. I feel a little shy about them

> because of problems of interpretation.

>

> Again, thanks Art, thanks Rich :)

>

> On Mon, May 21, 2018 at 11:34 PM, Rich Ulrich <

> rich-ulrich@

> > wrote:

>

>> There is testing, and there is model-fitting. We do, usually, want to

>> have

>> tests

>>

>> on the models, so we almost always want to meet the condition for

>> testing.

>>

>>

>> Measures with extreme skewness need to be transformed for least-squares

>>

>> statistics (ANOVA) or to be fitted with a non-linear maximum likelihood

>> models.

>>

>>

>> Remember that the assumption for least-squares testing is that equal

>> intervals

>>

>> of the scale should be equal in their influence (or in being influenced)

>> regardless

>>

>> of where they fall on the scale, be it the middle or an extreme. ("Equal

>> interval"

>>

>> describes a /relationship/, not the character of a single measure.)

>>

>>

>> Tukey gave a rule of thumb -- if the largest of a natural measurement

>> (non-negative)

>>

>> is 20 times the smallest, you almost always should use a transformation.

>> IIRC, "10 times"

>>

>> the smallest suggests that you should consider one. What you want to look

>> at first in

>>

>> choosing a transformation is not the skewness, however, but is the

>> mechanism for

>>

>> generating the numbers. For the first choices, counts imply square roots;

>> intensities

>>

>> imply logs (or logistic transforms); distances imply reciprocals.

>>

>>

>> --

>>

>> Rich Ulrich

>> ------------------------------

>> *From:* SPSSX(r) Discussion <

> SPSSX-L@.UGA

> > on behalf of

>> Florentino Jorge Menendez <

> fjmenendez@

> >

>> *Sent:* Monday, May 21, 2018 4:01:51 PM

>> *To:*

> SPSSX-L@.UGA

>> *Subject:* Fwd: A basic question about outliers

>>

>>

>> I d´appreciate if somebody can answer a novice doubt.

>>

>> Box plots mark a series of observations as outliers. That is clear in a

>> normal distribution: those cases that are more than 1.5 interquartile

>> ranges above P75 or below P25 are considered outliers (some authors say

>> 2.2

>> IR instead of 1.5 IR). That makes sense for me.

>>

>> But I don´t know how to consider outliers in skewed distributions. The

>> meaning of outliers comes from lie outside: we are trying to analyse if

>> observations belong to a distribution.

>>

>> But in a skewed distribution a lot of observations are above 1.5 or 2.2

>> or

>> more interquartile ranges and belong to the distribution... I feel

>> confused. Does it make any sense to talk about outliers in skewed

>> distributions? How to identify them?

>>

>> I´d appreciate any help. Thanks in advance.

>> Florentino Menéndez.

>>

>>

>> <

http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>>> Libre

>> de virus. www.avg.com

>> <

http://www.avg.com/email-signature?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=webmail>>> <#m_-4902436190330674248_x_m_6938251499252016520_DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2>

>>

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