RE: st: Robust regression - outliers

[Cameron McIntosh <cnm100@hotmail.com>]

Abdul,
It might not hurt to have a look at some of this literature too:
Oyeyemi, G.M., & Ipinyomi, R.A. (2010). A robust method of estimating covariance matrix in multivariate data analysis. African Journal of Mathematics and Computer Science Research, 3(1), 001-018.http://www.academicjournals.org/ajmcsr/PDF/pdf2010/Jan/Oyeyemi%20and%20Ipinyomi.pdf
Wu, G., Chen, C., & Yan, X. (2011). Modified minimum covariance determinant estimator and its application to outlier detection of chemical process data. Journal of Applied Statistics, 38(5), 1007-1020. 
Cerioli, A. (2010). Multivariate Outlier Detection With High-Breakdown Estimators. Journal of the American Statistical Association, 105(489), 147-156. 
Béguin, C., & Hulliger, B. (2004). Multivariate Outlier Detection in Incomplete Survey Data: The Epidemic Algorithm and Transformed Rank Correlations. Journal of the Royal Statistical Society, Series A (Statistics in Society), 167(2), 275-294.
Béguin, C., & Hulliger, B. (2008). The BACON-EEM algorithm for multivariate outlier detection in incomplete survey data. Survey Methodology, 34(1), 91-103. http://www.statcan.gc.ca/pub/12-001-x/2008001/article/10616-eng.pdf
Grossi, L., & Laurini, F. (2011). Robust estimation of efficient mean–variance frontiers. Advances in Data Analysis and Classification, 5(1), 3-22.
Todorov, V., Templ, M., & Filzmoser, P. (2011). Detection of multivariate outliers in business survey data with incomplete information. Advances in Data Analysis and Classification, 5(1), 37-56.http://www.springerlink.com/content/4t024018506q3267/fulltext.pdf
Todorov, V. (May 8, 2011). Scalable Robust Estimators with High Breakdown Point for Incomplete Data: Package ‘rrcovNA’, Version 0.4-02.http://cran.r-project.org/web/packages/rrcovNA/index.htmlhttp://cran.r-project.org/web/packages/rrcovNA/rrcovNA.pdf
Cam
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> Date: Thu, 22 Sep 2011 19:00:09 +0100
> Subject: Re: st: Robust regression - outliers
> From: njcoxstata@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> This presupposes that outliers are easily defined, which isn't always
> true. For example, it is not even always true that an outlier is
> associated with a large residual, but as you mention robust regression
> let's start optimistic.
>
> In my view the best ways to look for outliers are graphical.
>
> . search modeldiag
>
> and read the 2004 article and install the updated software for some
> suggestions, The article is directly accessible at
>
> http://www.stata-journal.com/sjpdf.html?articlenum=gr0009
>
> For example, -rvfplot2- in this package works after more
> regression-type commands than the official -rvfplot-.
>
> -extremes- is from SSC and not especially helpful here.
>
> Please note that the request to explain where user-written software
> you refer to come from applies to you too. (See also your last post.)
>
> Nick
>
> On Thu, Sep 22, 2011 at 6:46 PM, Ozgur Ozdemir <ozdemirozgur@hotmail.com> wrote:
>
> > I am trying to ding outliers in my regression analysis but none of the proposed commands such as cooksd, rstudent, hat etc works after a robust regression. Is there any specific command to run to find the outliers? I have also seen the extremes command but not sure if i can use. thanks
>
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