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Re: st: How to detect outliers


From   Xixi Lin <winnielxx@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: How to detect outliers
Date   Tue, 12 Feb 2013 15:35:57 -0500

Thanks Nick.

On Tue, Feb 12, 2013 at 2:50 PM, Nick Cox <njcoxstata@gmail.com> wrote:
> An -xi:- prefix is irrelevant here. You have nothing that requires
> -xi:-. The incantation is not needed, but does no harm. It so happens
> that all the -mmregress- examples use -xi:- but that's part accident.
>
> More to the point, the help file for -mmregress- explains how to get
> some residuals.
>
> I suggest you read the paper on -mmregress- to see what it does and
> doesn't do. Statalist is a discussion list, not a help line, and you
> are asked to look at documentation first.
>
> Nick
>
> On Tue, Feb 12, 2013 at 6:22 PM, Xixi Lin <winnielxx@gmail.com> wrote:
>
>> About the robust regression, I have a question, after running mmreg,
>> is it possible to predict residuals? Mine has errors:
>>
>> xi: mmregress Y X1 X2 X3
>> predict r,residual
>> error message: option residual not allowed
>>
>> My question is that is it possible to test residual normality and
>> heterokedasticity after robust regression or does robust regression
>> already corrects for those?
>
> On Mon, Feb 11, 2013 at 5:51 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
>
>>> Identifying outliers on the basis of a least squares fit is a very bad
>>> idea, however popular (Hampel et al., 1986). A far superior approach in
>>> Stata is the robust regression package -mmregress- by Verardi and Croux
>>> (-findit-). In providing a resistant fit, -mmregress- also identifies
>>> outliers and high leverage points.
>
>>>  Verardi, V., and C. Croux. 2009. Robust regression in Stata. Stata
>>> Journal 9, no. 3: 439-453.
>>>
>>> Hampel, Frank, Elvezio Ronchetti, Peter Rousseeuw, and Werner Stahel.
>>> 1986. Robust Statistics: The Approach Based on Influence Functions
>>> (Wiley Series in Probability and Mathematical Statistics). New York:
>>> John Wiley and Sons.
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