Re: st: outliers

[Jorge Eduardo Pérez Pérez <perez.jorge@ur.edu.co>]

I remember there was an outlier detection analysis that could be run
before fitting a model

-h hadimvo-

It is not an official part of Stata now, though. I wonder why ...


_______________________
Jorge Eduardo Pérez Pérez




On Fri, Aug 27, 2010 at 11:42 AM, Fabio Zona <fabio.zona@unibocconi.it> wrote:
> Wow! ..it seems to be a very long-lived, old story...
>
>
> ----- Messaggio originale -----
> Da: "Nick Cox" <n.j.cox@durham.ac.uk>
> A: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
> Inviato: Venerdì, 27 agosto 2010 17:27:38 GMT +01:00 Amsterdam/Berlino/Berna/Roma/Stoccolma/Vienna
> Oggetto: RE: st: outliers
>
> In 1827 Olbers asked Gauss "What should count as an unusual or too large a deviation? I would like to receive more precise directions." Gauss in his reply was disinclined to give any more directions and compared the situation to everyday life, where one often has to make intuitive judgments outside the reign of formal and explicit rules.
>
> This is a paraphrase of a paraphrase from Gigerenzer, G. and five friends. 1989. The empire of chance: How probability changed science and everyday life. Cambridge University Press, p.83, who give the reference to Olbers' Leben und Werke.
>
> Nick
> n.j.cox@durham.ac.uk
>
> Maarten buis
>
> --- On Fri, 27/8/10, fabio.zona@unibocconi.it wrote:
>> More broadly: when would you suggest to use mmregress
>> instead of regress (also with robust option)? Can we say
>> that mmregress is always better than the simple OLS? Or it
>> can be used only in the presence of a large number of
>> outliers? and for how many outliers would you suggest the
>> mmregres instaead of regress?
>
> Unfortunately there can be no general recipe we can follow
> here. Remember that what we are trying to do is the following:
> We have a question, we observe stuff, we summerize the stuff
> using a model, we answer our question based on that summary.
>
> Outliers are just observations that don't fit well in our
> model. This can mean two things, either there is something
> wrong witht the observations or there is something wrong with
> the model.
>
> There are several ways in which a computer can quantify how
> well an observation fits within the model, but there is no way
> a computer can decide whether it is the model or the observation
> that is to blame.
>
> The solution is to know your data, figure out why a certain
> observations have been classified as outliers. If you have many
> of those, don't only focus on various forms of "robust"
> regression, also consider that variables may have non-linear
> effects, i.e. try transformations. That is the art of using
> statistics for research.
>
>
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