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Ordinal regression

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


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Journal ArticleDOI
TL;DR: A new discrete statistical model for ordered categorical data is proposed via fixed-point discretization of a beta latent variable, providing new tools for the analysis of structured, clustered or longitudinal ordinal data.
Abstract: In this paper, a new discrete statistical model for ordered categorical data is proposed via fixed-point discretization of a beta latent variable. The resulting discretized beta distribution has a highly flexible shape and it can be either over-dispersed or under-dispersed with respect to the binomial distribution. It has only two parameters, which may therefore parsimoniously depend on covariates and on random effects, providing new tools for the analysis of structured, clustered or longitudinal ordinal data. Practical examples and advices are given and an application of the new model to subjective evaluations of a gastrointestinal disease is shown.

17 citations

Journal ArticleDOI
TL;DR: This simulation study compares the performance of the MNI and ordinal imputation regression model for incomplete longitudinal ordinal data for situations covering various numbers of categories of the ordinal outcome, time occasions, sample sizes, rates of missingness, well-balanced, and skewed data.
Abstract: Multiple imputation (MI) is now a reference solution for handling missing data. The default method for MI is the Multivariate Normal Imputation (MNI) algorithm that is based on the multivariate normal distribution. In the presence of longitudinal ordinal missing data, where the Gaussian assumption is no longer valid, application of the MNI method is questionable. This simulation study compares the performance of the MNI and ordinal imputation regression model for incomplete longitudinal ordinal data for situations covering various numbers of categories of the ordinal outcome, time occasions, sample sizes, rates of missingness, well-balanced, and skewed data.

17 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigated the effects of Internet usage comfort, Internet technical comfort, and demographic variables (gender, age, education, and income) on two key online transactional behaviors: online shopping and online banking.
Abstract: The purpose of this study was to investigate the effects of Internet usage comfort, Internet technical comfort, and demographic variables (gender, age, education, and income) on two key online transactional behaviors: online shopping and online banking. An ordinal regression model was used to test the effects of these variables. Findings show that Internet usage comfort and Internet technical comfort had significant and positive effects on both online shopping and online banking. Among the four demographic variables, only income had a significant and positive effect on online shopping, but both income and age had significant effects on online banking, the former positively and the latter negatively.

17 citations

DOI
01 Jan 2015
TL;DR: In this paper, the authors examined whether sudden, big jumps in women's presence in parliament are sufficient for improving women's ability to govern in the two cases with pre and post test data: Iraq and Spain.
Abstract: In this article, I examine whether sudden, big jumps in women’s presence in parliament are sufficient for improving beliefs in women’s ability to govern in the two cases with pre and posttest data: Iraq and Spain I explain why big jumps lend themselves to tests of sufficiency, defend the theory confirming advantages of this analytic approach and discuss the advantages of the Iraqi and Spanish comparison The Iraqi and Spanish tests of sufficiency include pre and posttest models of the effect of the big jump using ordinal regression analysis The analyses are run over 5026 Iraqi respondents and 2411 Spanish respondents to surveys administered before and after the big jumps The analysis confirms that big jumps are sufficient for improvement of beliefs in women’s ability to govern in Iraq and Spain

17 citations

Journal ArticleDOI
TL;DR: An ordinal model based on the Support Vector Ordinal Regression with Implicit Constraints was selected because of its ability to classify the patterns into the clusters, confirming the appropriateness of the clusters and their ordinal nature.
Abstract: The paper is focused on the progress toward knowledge economy (KE) of countries.Behaviour patterns on KE of the countries (clusters) are identified.Three ordinal classifiers are compared to identify the best model.The best ordinal model according its accuracy is chosen.The model can predict the classification toward KE of countries. Knowledge is a key factor of competitive advantages in the current economic crisis and uncertain environment. There are a number of indicators to measure knowledge advances, however, the benefits for stakeholders and policy makers are limited because of a lack of classification models. This paper introduces an approach to classify 54 countries (in 2007-2009) according to their progress toward a knowledge economy (KE). To achieve this, the aims of this paper are twofold: first, to find clusters of countries at a similar stage of development toward KE to test if they are meaningful; hence, it will be possible to order the clusters from early KEs (last cluster) to advanced KEs (first cluster). Second, having obtained these clusters, it is possible to build various models to detect the advancement of countries toward KE from one year to another due to its classification. Then, three ordinal classifiers from the machine-learning field were compared in order to select the classifier that performs the best and to confirm the ordinal description of the clusters. Finally, an ordinal model based on the Support Vector Ordinal Regression with Implicit Constraints was selected because of its ability to classify the patterns into the clusters, confirming the appropriateness of the clusters and their ordinal nature. The proposed ordinal classifier could be used for monitoring the progress or stage of transition to KE and for analysing whether a country changes clusters, entering one that performs better or worse.

17 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023102
2022191
202188
202093
201979
201873