>From what I've read (which isn't much), the idea is to estimate a utility (preference) function for discrete categories, using logistic regression, under the assumption that the residuals of the linear predictor of the utilities are ~ Type I Gumbel. This implies the "independence of irrelevant alternatives" in economic jargon. ie the utility of choice a versus choice b is independent of the introduction of a third choice c. It also implies homoscedasticity of the errors. The model can be generalized in various ways. If you are willing to introduce extra parameters into the model, such as the parameters of the Gumbel distribution, you may get more precision in the estimates of the utility function. An alternative (without the independence of irrelevant alternatives assumption) is to model the errors as multivariate normal (ie use probit regression), which is computationally much more difficult.
Whether it makes substantive sense to use these models outside of "discrete choice" experiments is another question. Patenting these methods is worrying. There have been a lot of people working on discrete choice experiments over the years. It's hard to believe that a single company could have ownership over an idea that is the result of a collaborative effort such as this. Cheers, Simon. On Thu, 2007-04-26 at 12:29 +1000, Tim Churches wrote: > This news item in a data mining newsletter makes various claims for a > technique called "Reduced Error Logistic Regression": > http://www.kdnuggets.com/news/2007/n08/12i.html > > In brief, are these (ambitious) claims justified and if so, has this > technique been implemented in R (or does anyone have any plans to do so)? > > Tim C > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.