On Fri, 21 Aug 2009, Noah Silverman wrote:
Hi,
For fun, I'm trying to throw some horse racing data into either an svm or lrm
model. Curious to see what comes out as there are so many published papers
on this.
One thing I don't know how to do is to standardize the probabilities by race.
This sounds closer to the conditional logit model.
However, if I recall correctly there is an assumption that in the models
of choice literature is stated something like 'independence of
alternatives that are unavailable'. That assumption might not hold in a
horse race where the speed at which a horse runs may depend on what horses
she is running against.
See
?survival:::clogit
and
@article{mcfadden1974conditional,
title={{Conditional logit analysis of qualitative choice behavior}},
author={McFadden, D.},
journal={Frontiers in econometrics},
volume={8},
pages={105--142},
year={1974}
}
BTW, Professor McFadden has a quintessentially American biography:
http://nobelprize.org/nobel_prizes/economics/laureates/2000/mcfadden-autobio.html
He mentions his personal background in farming and awards won for his
'sheep and geese', but alas does not mention horses or racing.
HTH,
Chuck
For example, if I train an LRM on a bunch of variable I get a model. I can
then get probability predictions from the model. That works.
It seems to me, that for a given race (8-12 horses) the probabilites of my
predictions should sum to one.
1) Is there some way to train the LRM to evaluate and then model the
subsequent date "per race"?? (Perhaps indicate some kind of grouping
variable?
2) Alternately, if I just run my data through a "standard" LRM, is there some
way to then "normalize" the probabilities in a correct way for each upcoming
race?
I've done some extensive research in this area and would be willing to
discuss more details offline with someone if they could contribute to the
process.
Thanks!!
-N
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Charles C. Berry (858) 534-2098
Dept of Family/Preventive Medicine
E mailto:cbe...@tajo.ucsd.edu UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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