Monthly Archives: March 2009

Discrete Choice Models

This is a broad and rich topic. Applications are found in almost every field. Over the past 30+ years major theoretical contributions from Econometrics, Psychometrics and Statistics have established the topic as a vibrant research area. Most major statistics oriented software packages provide most of the basic functionality. But sometimes this doesn’t go far enough. Sometimes real world models are defined with just enough complication that one can’t cast the model(s) of interest within the user interface provided by most software. Of course the solution is to step out from those software constraints and code the solution that is needed. Elsewhere in this blog there are software options that may be of use, and sometimes one needs access to codes at a more fundamental level. Another issue is that many researchers are not as familiar with the topic as they wish to be, but would otherwise like to know more. University of Calif. Economics Prof. Kenneth Train has provided both Gauss and Matlab codes addressing many Discrete Choice Models. In addition his site has about 20+ hours of lectures available for streaming download.

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Clustering Software reviews

Cluster analysis has been a data mining tool for some time. There are hundreds of cluster algorithms that compete for various statistical notions of performance. All major statistical software packages offer several solutions to address this task. Recently the notion of Latent Class Analysis has its version in the cluster analysis problem setting, where the unknown number of classes or groups is treated in either a stochastic or deterministic manner. In The American Statistician Feb 2009, Vol. 63 article, Review of Three Latent Class Cluster Analysis Packages: Latent Gold, poLCA, and MCLUST, one finds yet another discussion and comparison of the ever expanding software choices. The point of this note is the solution offered by the MCLUST program is available as free R software of the highest quality and performance. MCLUST performs model based clustering with multivariate normal mixtures. A Bayesian treatment of the latent class problem by MCLUST treats the unknown number of classes/groups as a random variable and its marginal posterior distribution of the number of classes is an outcome!

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