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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: MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models, used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design.

403 citations

Journal ArticleDOI
TL;DR: Four approaches to factor analysis of ordinal variables which take proper account of Ordinality are described and three of them are compared with respect to parameter estimates and fit and the issue of how to test the model and to measure model fit is discussed.
Abstract: Theory and methodology for exploratory factor analysis have been well developed for continuous variables. In practice, observed or measured variables are often ordinal. However, ordinality is most often ignored and numbers such as 1, 2, 3, 4, representing ordered categories, are treated as numbers having metric properties, a procedure which is incorrect in several ways. In this article we describe four approaches to factor analysis of ordinal variables which take proper account of ordinality and compare three of them with respect to parameter estimates and fit. The comparison is made both in terms of their relative methodological advantages and in terms of an empirical data example and two generated data examples. In particular, we discuss the issue of how to test the model and to measure model fit.

380 citations

Proceedings Article
01 Jan 2002
TL;DR: Two main approaches to the problem of ranking k instances with the use of a "large margin" principle are introduced: the "fixed margin" policy in which the margin of the closest neighboring classes is being maximized and a direct generalization of SVM to ranking learning.
Abstract: We discuss the problem of ranking k instances with the use of a "large margin" principle. We introduce two main approaches: the first is the "fixed margin" policy in which the margin of the closest neighboring classes is being maximized — which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for k - 1 different margins where the sum of margins is maximized. This approach is shown to reduce to v-SVM when the number of classes k - 2. Both approaches are optimal in size of 2l where l is the total number of training examples. Experiments performed on visual classification and "collaborative filtering" show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.

367 citations

Proceedings ArticleDOI
20 Jun 2011
TL;DR: The experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.
Abstract: In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.

349 citations

Book
01 Jan 2012
TL;DR: In this paper, the authors present a model selection loglinear analysis and a multinomial logistic regression model for least squares regression. But the model selection is not a generalization of linear models.
Abstract: 1. Model Selection Loglinear Analysis 2. Logit Loglinear Analysis 3. Multinomial Logistic Regression 4. Ordinal Regression 5. Probit Regression 6. Kaplan-Meier Survival Analysis 7. Life Tables 8. Cox Regression 9. Variance Components 10. Linear Mixed Models 11. Generalized Linear Models 12. Generalized Estimating Equations 13. Generalized Linear Mixed Models 14. Nonlinear Regression 15. Two-Stage Least-Squares Regression 16. Weighted Least-Squares Regression 17. Multidimensional Scaling

338 citations


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