This paper describes an R package, rpartOrdinal, that implements alternative splitting functions for fitting a classification tree when interest lies in predicting an ordinal response. This includes the generalized Gini impurity function, which was introduced as a method for predicting an ordinal response by including costs of misclassification into the impurity function, as well as an alternat...

We study strategic games where players' preferences are weak orders which need not admit utility representations. First of all, we extend Voorneveld's concept of best-response potential from cardinal to ordinal games and derive the analogue of his characterization result: An ordinal game is a best-response potential game if and only if it does not have a best-response cycle. The characterizatio...

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This paper focuses on developing models to study influential factors on the inflation rate for a panel of available countries in the World Bank data base during 2008-2012‎. ‎For this purpose‎, Random effect log-linear and Ordinal logistic models are used for the analysis of continuous and categorical inflation rate variables‎. ‎As the original inflation rate response to variables shows an appar...

How can we grasp the temporal structure of events? A few studies have indicated that representations of temporal structure are acquired when there is an intention to learn, but not when learning is incidental. Response-to-stimulus intervals, uncorrelated temporal structures, unpredictable ordinal information, and lack of metrical organization have been pointed out as key obstacles to incidental...

This article reviews methodologies used for analyzing ordered categorical (ordinal) response variables. We begin by surveying models for data with a single ordinal response variable. We also survey recently proposed strategies for modeling ordinal response variables when the data have some type of clustering or when repeated measurement occurs at various occasions for each subject, such as in l...

Ordinal logistic regression is a statistical analysis method that can be used to model the relationship between an ordinal response variable and one or more explanatory variables. An ordinal variable is a categorical variable for which there is a clear ordering of the category levels. The explanatory variables may be either continuous or categorical. Estimating ordinal logistic regression model...

In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ord...