Ordinal regression

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

An experimental study of different ordinal regression methods and measures

Abstract: In this paper, an experimental study of different ordinal regression methods and measures is presented The first objective is to gather the results of a considerably high number of methods, datasets and measures, since there are not many previous comparative studies of this kind in the literature The second objective is to detect the redundancy between the evaluation measures used for ordinal regression The results obtained present the maximum MAE (maximum of the mean absolute error of the difference between the true and the predicted ranks of the worst classified class) as a very interesting alternative for ordinal regression, being the less uncorrelated with respect to the rest of measures Additionally, SVOREX and SVORIM are found to yield very good performance when the objective is to minimize this maximum MAE

A multivariate model for ordinal trait analysis.

Abstract: Many economically important characteristics of agricultural crops are measured as ordinal traits Statistical analysis of the genetic basis of ordinal traits appears to be quite different from regular quantitative traits The generalized linear model methodology implemented via the Newton–Raphson algorithm offers improved efficiency in the analysis of such data, but does not take full advantage of the extensive theory developed in the linear model arena Instead, we develop a multivariate model for ordinal trait analysis and implement an EM algorithm for parameter estimation We also propose a method for calculating the variance-covariance matrix of the estimated parameters The EM equations turn out to be extremely similar to formulae seen in standard linear model analysis Computer simulations are performed to validate the EM algorithm A real data set is analyzed to demonstrate the application of the method The advantages of the EM algorithm over other methods are addressed Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population

Current prospects on ordinal and monotonic classification

Abstract: Ordinal classification covers those classification tasks where the different labels show an ordering relation, which is related to the nature of the target variable. In addition, if a set of monotonicity constraints between independent and dependent variables has to be satisfied, then the problem is known as monotonic classification. Both issues are of great practical importance in machine learning. Ordinal classification has been widely studied in specialized literature, but monotonic classification has received relatively low attention. In this paper, we define and relate both tasks in a common framework, providing proper descriptions, characteristics, and a categorization of existing approaches in the state-of-the-art. Moreover, research challenges and open issues are discussed, with focus on frequent experimental behaviours and pitfalls, commonly used evaluation measures and the encouragement in devoting substantial research efforts in specific learning paradigms.

Effect of dropouts in a longitudinal study: an application of a repeated ordinal model.

Abstract: An analysis is presented of a longitudinal study of fluvoxamine, an antidepressant drug, with ordinal responses, regressed on a combination of discrete and continuous covariates and with a substantial proportion of dropouts. Classical methods, such as weighted least squares (SAS procedure CATMOD) and logistic regression, are not suitable for the analysis of such data. Instead, we illustrate how a recently introduced model can be used to solve most of the problems posed. The method is likelihood-based and is an extension of the bivariate Dale model to an arbitrary number of outcomes. The method is suitable for several types of designs commonly employed in clinical trials.