On Mon, 31 Aug 2009, Noah Silverman wrote:
Um..... I did my research. Have been for years. I assume you're referring
to Boltman and Chapmanm "A multinomial logit model for handicapping horse
races" included in "Efficiency of racetrack betting markets". Page 155
references what they call a "multinomial model". From equation 14 in their
paper, it appears as if they are calculating "Utility" of a horse as a
number. Far from what I understand a traditional "Multinomial" model is.
The seminar that I referenced discussed using a probit model instead of a
logit model. Since the Boltman and Chapman application didn't really have
multiple discreet choices, I'm not sure how the probit model would. Hence my
inquiry.
But of course it has multiple choices (finishing ranks instead of binary
win/lose), otherwise it wouldn't be very multinomial, would it?
Z
On 8/31/09 6:23 PM, Achim Zeileis wrote:
On Mon, 31 Aug 2009, Noah Silverman wrote:
I get that.
Still trying to figure out what the "multi" nominal labels they used were.
That's why I passed on the reference to the seminar summary.
So that I could do the research for you? Come on...the usual strategy
applies: Look at the references! (Hint: The information is in the Bolton
and Chapman paper.)
Z
On 8/31/09 5:40 PM, Achim Zeileis wrote:
On Mon, 31 Aug 2009, Noah Silverman wrote:
Thanks Achim,
I discovered the Journal article just after posting this question. It
did help explain more.
My original inspiration for looking at this package came from a seminar
"summary" given in 2002. Unfortunately , I can not find any actual
published paper or lecture notes that explain the lecturer's application
of the MNP.
Here is a link to the PDF of the summary:
http://www-stat.stanford.edu/seminars/stat/abstracts2001-2002/gu.pdf
Most of the other published research on using logit or probit models for
horseracing data use a binary label of win/lose. So, my thought was
that they were using the same for this application.
Any thoughts?
As I said in my last mail: *Multi*nomial probit typically conveys more
than 2 choices while *bi*nomial probit conveys exactly 2 choices.
Z
--
Noah
On 8/31/09 5:07 PM, Achim Zeileis wrote:
On Mon, 31 Aug 2009, Noah Silverman wrote:
Hello,
I want to start testing using the MNP probit function in stead of the
lrm function in my current experiment.
I have one dependant label and two independent varaibles.
The lrm is simple
model <- lrm(label ~ val1 + val2)
I tried the same thing with the mnp function and got an error that I
don't understand
model <- mnp(label ~ val1 + val2)
I get back an immediate error that tells me, "The number of
alternatives should be at least 3"
Since I have a binary training label, this looks like a problem.
(Additionally, I thought that a probit was a appropriate tool for
building binary models.)
Any advice?
*Multi*nomial probit typically conveys more than 2 choices while
*bi*nomial probit conveys exactly 2 choices. One could argue that the
latter should be a special case of the former but the more general case
has much more computational challenges.
The =2 vs >2 information might have been inferred from the title of the
package already but if you wanted to take extreme actions you could
read the mnp() manual page or oven the references it points you to: The
software is discussed in the Journal of Statistical Software
(http://www.jstatsoft.org/v14/i03/) and the theory is described in an
article in the Journal of Econometrics (124, 311-334).
Z
Thanks!
-N
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