Take a look at the updated version of Bradley Efron's 2004 ASA Presidential Address:
I find it a clear, concise, and obviously authoritative statement of the important concepts of EBE and FDR.
From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Ryan
Sent: Thursday, October 23, 2008 7:49 AM
Subject: Re: GLIMMIX 9.2 documentation question
On Oct 23, 3:23 am, Oliver.K...@medizin.uni-halle.de wrote:
> On 23 Okt., 04:45, Ryan <Ryan.Andrew.Bl...@gmail.com> wrote:
> > Hello,
> > I've been spending quite some time in GLIMMIX documentation, and
> > came across a statement that has confused me. The statement below
> > comes from the article, "Growing Up Fast: SAS 9.2 Enhancements in
> > the GLIMMIX procedure."
> > * The quadrature rule in the GLIMMIX procedure is adaptive in the
> > following sense: ...
> > - The GLIMMIX procedure centers and scales the quadrature points by
> > using the empirical Bayes estimates (EBEs) of the random effects and
> > the Hessian (second
> > derivative) matrix from the EBE
> > suboptimization to improve the likelihood approximation.
> > -----------------------
> > I'm a bit confused by the use of the term "empirical Bayes estimates
> > (EBE)." I didn't think Bayesian estimation was used in this
> > procedure. Could anyone shed some light on this? What would be the
> > benefits of using EBE?
> > Thanks,
> > Ryan
> Hello Ryan,
> you are right, PROC GLIMMIX does not use "real" Bayesian Estimation in
> the sense of MCMC or something like that. My understanding is that the
> implemented algorithm for numerical quadrature in GLIMMIX (it is the
> same with PROC NLMIXED) is an iterative one which needs updated
> estimates for the random effects in each step. These random effects
> estimates are estimated by the empirical Bayes method. Estimation is
> not very complicated here, estimates result from a simple
> computational step. As such, empirical Bayes is just an estimation
> principle which is used and has proven sensible.
> Hope that helps,
> Oliver- Hide quoted text -
> - Show quoted text -
It's good to hear from you, & thank you for responding!
So, this is not comparable to other bayesian estimation techniques? What exactly makes EBE useful? Does it simply improve efficiency of the estimation process or does it, for instance, improve the precision of estimates in specific circumstances?