One of the strengths of the model is its competitive grade of interpretability which is a requisite in most of real applications. The proposed algorithm is compared to other well-known ordinal ...

We argue for the application of ordinal models that make appropriate assumptions about the variables under study. In this tutorial article, we first explain the three major ordinal model classes; the ...

An illustration of a computer application window Wayback Machine. An illustration of an open book. Books. An illustration of two cells of a film strip. Video An ... ERIC ED504366: Using Ordinal ...

We will present the assumed theoretical model behind logistic regression, implement it using R's glm, and then show how to interpret its results, perform predictions, and (nested) model comparisons.

The adjacent-categories, continuation-ratio and proportional odds logit-link regression models provide useful extensions of the multinomial logistic model to ordinal response data. We propose fitting ...

Next, we cover the ordinal logistic regression model, specifically the cumulative logit ordinal regression model, which is used for the ordinal outcome data. We then cover the case of the categorical, ...

This paper introduces a new instance-based algorithm for multiclass classification problems where the classes have a natural order. The proposed algorithm extends the state-of-the-art gravitational ...