Topic
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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TL;DR: A natural generalization of the Wilcoxon-Mann-Whitney statistic, which now corresponds to the volume under an r-dimensional surface (VUS) for r ordered categories and differs from extensions recently proposed for multi-class classification.
106 citations
TL;DR: In this article, the authors present a general framework for associating value or worth with ordinal ranks, and develop models for deriving a consensus based on this framework, showing that the lp distance models using this framework are equivalent to the conventional ordinal models for any p ⩾ 1.
105 citations
TL;DR: In this paper, a new method, called ELECTREGKMS, which employs robust ordinal regression to construct a set of outranking models compatible with preference information, is presented, where preference information supplied by the decision maker is composed of pairwise comparisons stating the truth or falsity of the outranking relation for some real or fictitious reference alternatives.
104 citations
TL;DR: In this paper, a simple specification for ordinal qualitative dependent variable models is developed and a consistent asymptotically normal estimator is offered, compared to the conventional dummy variable approach using simulated data.
104 citations
17 Jan 2014
TL;DR: In this article, the basic principle of robust ordinal regression and the main multiple criteria decision methods to which it has been applied are described, in particular, UTA GMS and GRIPmethods, dealing with choice and ranking problems, then UTADIS GMS, dealing with sorting (ordinal classification) problems.
Abstract: Within disaggregation–aggregation approach, ordinal regressionaims at inducing parameters of a preference model, for example, parameters of a value function, which represent some holistic preference comparisons of alternatives given by the Decision Maker (DM). Usually, from among many sets of parameters of a preference model representing the preference information given by the DM, only one specific set is selected and used to work out a recommendation. For example, while there exist many value functions representing the holistic preference information given by the DM, only one value function is typically used to recommend the best choice, sorting, or ranking of alternatives. Since the selection of one from among many sets of parameters compatible with the preference information given by the DM is rather arbitrary, robust ordinal regressionproposes taking into account all the sets of parameters compatible with the preference information, in order to give a recommendation in terms of necessary and possible consequences of applying all the compatible preference models on the considered set of alternatives. In this chapter, we present the basic principle of robust ordinal regression, and the main multiple criteria decision methods to which it has been applied. In particular, UTA GMS and GRIPmethods are described, dealing with choice and ranking problems, then UTADIS GMS , dealing with sorting (ordinal classification) problems. Next, we present robust ordinal regression applied to Choquet integral for choice, sorting, and ranking problems, with the aim of representing interactions between criteria. This is followed by a characterization of robust ordinal regression applied to outranking methods and to multiple criteria group decisions. Finally, we describe an interactive multiobjective optimization methodology based on robust ordinal regression, and an evolutionary multiobjective optimization method, called NEMO, which is also using the principle of robust ordinal regression.
103 citations