Journal of Data Science

Abstract: Relative entropy identities yield basic decompositions of cat egorical data log-likelihoods. These naturally lead to the development of information models in contrast to the hierarchical log-linear models. A recent study by the authors clarified the principal difference in the data likelihood analysis between the two model types. The proposed scheme of log-likelihood decomposition introduces a prototype of linear information models, with which a basic scheme of model selection can be formulated accordingly. Empirical studies with high-way contingency tables are exem plified to illustrate the natural selections of information models in contrast to hierarchical log-linear models.

Abstract: Applications of multivariate statistical techniques, including graphical models, are seldom found in e-commerce studies. However, as this paper demonstrates, we find that probabilistic graphical models are useful in this area, both because of their ability to handle large numbers of potentially interrelated variables, and because of their ability to communicate statistical relationships clearly to both the researcher and the ultimate business audience. We show an application of this methodology to intranets, internal corporate information systems employing Internet technology. In particular, we study both the interrelationships among intranet benefits and the interrelationships among intranet applications. This approach confirms some hypothesized relationships, and uncovers heretofore-unanticipated relationships among intranet variables, providing guidance for business professionals seeking to develop effective intranet systems. The techniques described here also have potential applicability in other e-commerce arenas, including business-to-consumer and business-to-business applications.