Date:         Thu, 1 Oct 2009 12:21:02 -0500
Reply-To:     Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US>
Subject:      Re: Model Comparison using AIC
Comments: To: Peter Flom <peterflomconsulting@mindspring.com>
Content-Type: text/plain; charset="iso-8859-1"

I agree that usually, we consider model averaging over a single sample.  But the poster's question appeared to suggest a cross-validation of the model.  I wondered if combining the two might lead to more robust parameter estimation.

It was just a thought.

Warren


-----Original Message-----
From: Peter Flom [mailto:peterflomconsulting@mindspring.com]
Sent: Thu 10/1/2009 11:45 AM
To: Warren Schlechte; SAS-L@LISTSERV.UGA.EDU
Subject: Re: Model Comparison using AIC

Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US> wrote

>Using different samples suggest a cross-validation approach.  Modeling
>averaging seems what is desired, and based on my reading, Burnham and
>Anderson (1998) use an Akaike weighting within the model averaging to
>get estimates of parameters.
>
>So, what I guess I'm saying is maybe the AIC can be used within a model
>averaging realm, which seems to be what is going on here.
>
>Obviously, I await responses from Dale and others.
>

Burnham and Anderson average various models on the same sample, not various samples
on the same model.

If you have several samples, then why not combine them to get greater power? You could, if
desired, include SAMPLE as covariate.


Peter

Peter L. Flom, PhD
Statistical Consultant
Website: www DOT peterflomconsulting DOT com
Writing; http://www.associatedcontent.com/user/582880/peter_flom.html
Twitter:   @peterflom