The problems with the various flavors of stepwise variable selection (including forward and backward selection) have been discussed quite exhaustively on Statalist (search the archives). In short, the models/variables selected by stepwise methods tend to be unstable and often do not replicate well.
Scott Millis
--- On Fri, 7/15/11, Allan Kennedy <dkstata@gmail.com> wrote:
> From: Allan Kennedy <dkstata@gmail.com>
> Subject: Re: st: Backward Selection (AIC) with xtmixed
> To: statalist@hsphsun2.harvard.edu
> Date: Friday, July 15, 2011, 10:53 PM
> Could you provide a deeper rationale
> as to why this approach is not
> recommended?/
>
> On Fri, Jul 15, 2011 at 10:44 PM, SR Millis <aa3379@wayne.edu>
> wrote:
> > This procedure is not recommended.
> >
> >
> > ~~~~~~~~~~~
> > Scott R Millis, PhD, ABPP, CStat, PStat®
> > Professor
> > Wayne State University School of Medicine
> > Email: aa3379@wayne.edu
> > Email: srmillis@yahoo.com
> > Tel: 313-993-8085
> >
> >
> > --- On Fri, 7/15/11, Allan Kennedy <dkstata@gmail.com>
> wrote:
> >
> >> From: Allan Kennedy <dkstata@gmail.com>
> >> Subject: st: Backward Selection (AIC) with
> xtmixed
> >> To: statalist@hsphsun2.harvard.edu
> >> Date: Friday, July 15, 2011, 10:36 PM
> >> Greetings,
> >>
> >> I am interesting is using an automated command
> that will
> >> estimate
> >> model fit with a backward selection model with AIC
> of an
> >> --xtmixed--
> >> approach. I know that both --stepwise-- and
> --swaic-- both
> >> do not
> >> support xtmixed but I wanted to see if there were
> any other
> >> user
> >> written programs that support --xtmixed--. An
> example of my
> >> code is as
> >> follows:
> >>
> >> xtmixed OUTCOME 10PREDICTORS CONTROL
> VARIABLES
> >> ||RANDOM_1:
> >> ||RANDOM_2: ||RANDOM_3: , covariance(unstructured)
> var mle
> >>
> >> I would like to estimate the lowest AIC model of
> any
> >> combination of
> >> the 10 predictors.
> >>
> >> Your assistance would be greatly appreciated.
> >>
> >>
> >> --AK
> >> *
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> >>
> >
> > *
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> >
>
> *
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>
*
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