In this paper we propose two versions of stochastic choice models based on tree structure models — called “tree unfolding models”. These models can be viewed as discrete (tree structure) analogues of a recently proposed class of continuous (spatial) random utility models for paired...

Two recently developed probabilistic multidimensional models for analyzing pairwise choice data are introduced, discussed in terms of their differential properties, and extended in several ways. The first one, the wandering vector model, was originally suggested by Carroll and extended by De...

Network analysis has become a popular method for identifying the communication structure in a system where positional and relational aspects are important. In this paper, a maximum likelihood based methodology is presented that allows for the analysis of binary sociometric data. This methodology...

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 the development of a new stochastic multidimensional (scaling) unfolding (Coombs 1964) methodology which operates on paired comparison consumer preference or choice data and renders a spatial representation of both consumers and the products or brands they choose. Consumers...

This paper presents a new stochastic three-way un folding method designed to analyze asymmetric three- way, two-mode binary data. As in the metric three- way unfolding models presented by DeSarbo (1978) and by DeSarbo and Carroll (1980, 1981, 1985), this procedure estimates a joint space of row...

We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s)....