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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: This paper investigates answers to the choice of the number of components most suitable for a given data set in the context of likelihood‐based clustering of the rows of a matrix of ordinal data modelled by the ordered stereotype model.
Abstract: Summary One of the key questions in the use of mixture models concerns the choice of the number of components most suitable for a given data set. In this paper we investigate answers to this problem in the context of likelihood-based clustering of the rows of a matrix of ordinal data modelled by the ordered stereotype model. Two methodologies for selecting the best model are demonstrated and compared. The first approach fits a separate model to the data for each possible number of clusters, and then uses an information criterion to select the best model. The second approach uses a Bayesian construction in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Simulation studies are presented which include a variety of scenarios in order to test the reliability of both approaches. Finally, the results of the application of model selection to two real data sets are shown.

9 citations

Journal ArticleDOI
TL;DR: This paper proposes an unimodal regularisation based on the beta distribution applied to the cross-entropy loss, which encourages the distribution of the labels to be a soft unimmodal distribution, more appropriate for ordinal problems.

9 citations

Journal ArticleDOI
TL;DR: A simulation study is used to illustrate how this model also has more general application for conventional short ordinal scores, to select amongst competing models of varying complexity for the cut-point parameters.
Abstract: In many medical studies, researchers widely use composite or long ordinal scores, that is, scores that have a large number of categories and a natural ordering often resulting from the sum of a number of short ordinal scores, to assess function or quality of life. Typically, we analyse these using unjustified assumptions of normality for the outcome measure, which are unlikely to be even approximately true. Scores of this type are better analysed using methods reserved for more conventional (short) ordinal scores, such as the proportional-odds model. We can avoid the need for a large number of cut-point parameters that define the divisions between the score categories for long ordinal scores in the proportional-odds model by the inclusion of orthogonal polynomial contrasts. We introduce the repeated measures proportional-odds logistic regression model and describe for long ordinal outcomes modifications to the generalized estimating equation methodology used for parameter estimation. We introduce data from a trial assessing two surgical interventions, briefly describe and re-analyse these using the new model and compare inferences from the new analysis with previously published results for the primary outcome measure (hip function at 12 months postoperatively). We use a simulation study to illustrate how this model also has more general application for conventional short ordinal scores, to select amongst competing models of varying complexity for the cut-point parameters.

9 citations

Journal ArticleDOI
TL;DR: A unified framework based on a general latent variable model for the comparison of treatments with ordinal responses is proposed, which is rich enough to include many important existing models for analyzing ordinal categorical variables, including the proportional odds model, the ordered probit-type model, and the proportional hazards model.
Abstract: Different latent variable models have been used to analyze ordinal categorical data which can be conceptualized as manifestations of an unobserved continuous variable. In this paper, we propose a unified framework based on a general latent variable model for the comparison of treatments with ordinal responses. The latent variable model is built upon the location-scale family and is rich enough to include many important existing models for analyzing ordinal categorical variables, including the proportional odds model, the ordered probit-type model, and the proportional hazards model. A flexible estimation procedure is proposed for the identification and estimation of the general latent variable model, which allows for the location and scale parameters to be freely estimated. The framework advances the existing methods by enabling many other popular models for analyzing continuous variables to be used to analyze ordinal categorical data, thus allowing for important statistical inferences such as location and/or dispersion comparisons among treatments to be conveniently drawn. Analysis on real data sets is used to illustrate the proposed methods.

9 citations


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