Distance-based logistic model for cross-classified categorical data.

Logistic regression models are a powerful research tool for the analysis of cross-classified data in which a categorical response variable is involved. In a logistic model, the effect of a covariate refers to odds, and the simple relationship between the coefficients and the odds ratio often makes these the parameters of interest due to their easy interpretation. In this article we present a distance-based logistic model that allows a simple graphical interpretation of the association coefficients using the odds ratio in a contingency table.

Distance stability analysis in multidimensional scaling using the jackknife method.

Stability or sensitivity analysis is an important topic in data analysis that has received little attention in the application of multidimensional scaling (MDS), for which the only available approaches are given in terms of a coordinate-based analytical jackknife methodology. Although in MDS the prime interest is in assessing the stability of the points in the configuration, this methodology may be influenced by imprecisions resulting from the inherently necessary Procrustes method. This paper proposes an analytical distance-based jackknife procedure to study stability and cross-validation in MDS in terms of the jackknife distances, which is not influenced by the Procrustes method.

A latent class distance association model for cross-classified data with a categorical response variable.

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic.