In article <92ab9t$dot$[EMAIL PROTECTED]>,
Robert Dodier <[EMAIL PROTECTED]> wrote:
> T.S. Lim <[EMAIL PROTECTED]> wrote:
>
> > In the software package Latent GOLD
> >
> > http://www.statisticalinnovations.com/lg/highlights.htm
> >
> > you can do regression (continuous or discrete response variable)
using
> > mixture modeling. I'm interested in the statistical model behind the
> > method. Anyone knows of (online) papers that discuss it? Thanks.
>
> Well, an obvious way to implement a regression using a mixture model
> is to construct a mixture density over all the variables (predictors
> and response) and then compute the conditional density of the response
> given the predictors. The latent variable in this kind of scheme is
> the indicator which tells to which bump each case is assigned.
>
> However, that doesn't appear to be the way that Latent GOLD (gotta
> love these trade names :) goes about it. It seems they work directly
> with a mixture of conditional densities without going through the
> intermediate step of constructing the joint density. Here, see for
> yourself: http://www.statisticalinnovations.com/lg/lg_app3.pdf
> This paper contains a number of references that may be helpful.
>
> For what it's worth,
> Robert Dodier
> --
> ``Socrates used to meditate all day in the snow, but Descartes'
> mind worked only when he was warm.'' -- Bertrand Russell
It appears that regression in Latent GOLD is based on
Wedel and DeSarbo. 1994. A review of recent developments in latent
class regression models, in Advanced Methods of Marketing Research
(Bagozzi, editor), pp. 352-388. Blackwell Publishers, Cambridge.
Wedel and Karnakura. 1998. Market Segmentation: Concept and
Methodological Foundations. Kluwer Academic Publishers, Boston.
--
T.S. Lim
[EMAIL PROTECTED]
www.Recursive-Partitioning.com
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