Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.

The analysis of longitudinal ordinal data with nonrandom drop-out

Abstract: A model is proposed for longitudinal ordinal data with nonrandom drop-out, which combines the multivariate Dale model for longitudinal ordinal data with a logistic regression model for drop-out Since response and drop-out are modelled as conditionally independent given complete data, the resulting likelihood can be maximised relatively simply, using the EM algorithm, which with acceleration is acceptably fast and, with appropriate additions, can produce estimates of precision The approach is illustrated with an example Such modelling of nonrandom drop-out requires caution because the interpretation of the fitted models depends on assumptions that are unexaminable in a fundamental sense, and the conclusions cannot be regarded as necessarily robust The main role of such modelling may be as a component of a sensitivity analysis

Evaluation Measures for Ordinal Regression

Abstract: Ordinal regression (OR -- also known as ordinal classification) has received increasing attention in recent times, due to its importance in IR applications such as learning to rank and product review rating. However, research has not paid attention to the fact that typical applications of OR often involve datasets that are highly imbalanced. An imbalanced dataset has the consequence that, when testing a system with an evaluation measure conceived for balanced datasets, a trivial system assigning all items to a single class (typically, the majority class) may even outperform genuinely engineered systems. Moreover, if this evaluation measure is used for parameter optimization, a parameter choice may result that makes the system behave very much like a trivial system. In order to avoid this, evaluation measures that can handle imbalance must be used. We propose a simple way to turn standard measures for OR into ones robust to imbalance. We also show that, once used on balanced datasets, the two versions of each measure coincide, and therefore argue that our measures should become the standard choice for OR.

Multivariate Analysis of Ordinal Variables

Abstract: This article examines the assumptions underlying two multivariate strategies commonly used in analyzing ordinal data Both strategies employ as a descriptive tool the ordinary multiple regression algorithms; the crucial difference between the two is that the first, ordinal strategy, uses the matrix of Kendall's 's as the building block of multivariate analysis, while the second, parametric strategy, uses the matrix of Pearson's 's These two strategies are evaluated and constrasted in terms of their usefulness in answering basic research questions that arise in multivariate analysis One overriding conclusion is that, contrary to the claims of its proponents, the ordinal strategy is no better than the parametric strategy at meeting some of the basic requirements of multivariate analysis It is argued that parametric strategy, when accompanied by careful evaluation of the validity of the implict quantification of ordinal variables, is more amenable to one of the goals of scientific research: successive app