[R] Logit Model... GLM or GEE or ??
rkoenker at uiuc.edu
Fri Aug 7 00:15:57 CEST 2009
You could take a look at:
M West, PJ Harrison, HS Migon - Journal of the American Statistical
Association, 1985 - jstor.org
Page 1. Dynamic Generalized Linear Models and Bayesian Forecasting
and the subsequent literature it has generated... or along the same
lines the literature on chess
url: www.econ.uiuc.edu/~roger Roger Koenker
email rkoenker at uiuc.edu Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Urbana, IL 61801
On Aug 6, 2009, at 5:00 PM, Noah Silverman wrote:
> Posted about this earlier. Didn't receive any response
> But, some further research leads me to believe that MAYBE a GLMM or
> a GEE function will do what I need.
> I have a bit of a tricky puzzle with trying to implement a logit
> model as described in a paper.
> The particular paper is on horseracing and they explain a model that
> is a logit trained "per race", yet somehow the coefficients are
> combined across all the training races to come up with a final set
> of coefficients.
> My understanding is that they maximize log likelihood across the
> entire set of training races. Yet this isn't just as standard logit
> model as they are looking at data "per race".
> This is a bit hard to explain, so I've attached a tiny pdf of the
> paragraph from the paper explaining this.
> Like everything else in the data/stat/econ world, there is probably
> a library in R that does this kind of thing, but after 3 days of
> heavy google research, I've been unable to find it.
> Does anyone have any suggestions??
> Attached is a jpg of the book page describing what I'm trying to do...
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> and provide commented, minimal, self-contained, reproducible code.
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