This paper provides a general Structural Equation finite Mixture Model and algorithm (STEMM). Substantively, the model allows the researcher to simul­taneously treat heterogeneity and form groups in the context of a postulated causal (i.e., simultaneous equation regression) structure in which...

This paper develops a maximum likelihood based method for simultaneously performing multidimensional scaling and cluster analysis on two-way dominance or profile data. This MULTICLUS procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of...

The vast majority of existing multidimensional scaling (MDS) procedures devised for the analysis of paired comparison preference/choice judgments are typically based on either scalar product (i.e., vector) or unfolding (i.e., ideal-point) models. Such methods tend to ignore many of the essential...

This paper presents a new stochastic multidimensional scaling procedure for the analysis of three-mode, three-way pick any/ J data. The method provides either a vector or ideal-point model to represent the structure in such data, as well as “floating” model specifications (e.g., different...

This paper develops a maximum likelihood based methodology for simultaneously performing multidimensional unfolding and cluster analysis on two-way dominance or profile data. This new procedure utilizes mixtures of multivariate conditional normal distributions to estimate a joint space of...

The ability to identify customers' dynamics and its drivers present an opportunity for firms to optimize their marketing resource allocation and increase long-run profitability. Accordingly, we address the following managerial questions in this research: (1) how can firms dynamically segment...

The predictive validity of a choice model is often assessed by its hit rate on estimation and holdout data. We examine and illustrate the conditions under which a higher likelihood values does not imply a higher hit rate. The results suggest that the hit rate should not be overly emphasized when...