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: Given processes that assign binary vectors to data, one wish to test models that simulate those processes and uncover groupings in the processes. It is shown that a suitable test can be derived from a kappa type agreement measure. This is applied to analyze stress placement in spoken phrases, based on experimental data previously obtained. The processes were Portuguese speakers and the grouping corresponds to the Brazilian and European varieties of that language. Optimality Theory gave rise to different models. The agreement measure was successful in pointing the relative fitness of models to language varieties.

Abstract: This article deals with the latent class analysis of models with