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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 study proposes conditional joint random-effects models, which take into account the inherent association between the continuous, binary and ordinal outcomes, and Simulation studies show that, by jointly modelling the trivariate outcomes, standard deviations of the estimates of parameters in the models are smaller and much more stable, leading to more efficient parameter estimates and reliable statistical inferences.
Abstract: In medical studies, repeated measurements of continuous, binary and ordinal outcomes are routinely collected from the same patient. Instead of modelling each outcome separately, in this study we propose to jointly model the trivariate longitudinal responses, so as to take account of the inherent association between the different outcomes and thus improve statistical inferences. This work is motivated by a large cohort study in the North West of England, involving trivariate responses from each patient: Body Mass Index, Depression (Yes/No) ascertained with cut-off score not less than 8 at the Hospital Anxiety and Depression Scale, and Pain Interference generated from the Medical Outcomes Study 36-item short-form health survey with values returned on an ordinal scale 1-5. There are some well-established methods for combined continuous and binary, or even continuous and ordinal responses, but little work was done on the joint analysis of continuous, binary and ordinal responses. We propose conditional joint random-effects models, which take into account the inherent association between the continuous, binary and ordinal outcomes. Bayesian analysis methods are used to make statistical inferences. Simulation studies show that, by jointly modelling the trivariate outcomes, standard deviations of the estimates of parameters in the models are smaller and much more stable, leading to more efficient parameter estimates and reliable statistical inferences. In the real data analysis, the proposed joint analysis yields a much smaller deviance information criterion value than the separate analysis, and shows other good statistical properties too.

17 citations

Journal ArticleDOI
TL;DR: Results indicate that operating unit size is related positively to the level of consideration for ABC, which implies that the availability of financial, labour, computing and time resources should mean that it is more likely for operating units to be considering or have considered ABC.
Abstract: Prior research into the extent to which operating units have considered activity-based costing (ABC) has either examined the extent to which operating units have considered or not considered ABC. This paper uses logistic ordinal regression analysis to examine the impact of the level of competition, product customization, manufacturing overhead costs and operating unit size on the level of consideration for ABC when measured on a three-point ordinal scale ranging from not considered, considering and considered ABC. The results indicate that operating unit size is related positively to the level of consideration for ABC. This implies that the availability of financial, labour, computing and time resources should mean that it is more likely for operating units to be considering or have considered ABC.

17 citations

Posted Content
TL;DR: This work characterize the smooth convex surrogates compatible with a given task loss in terms of a suitable Bregman divergence composed with a link function to derive tight bounds for the calibration function and to obtain novel results on existing surrogate frameworks for structured prediction such as conditional random fields and quadratic surrogates.
Abstract: In this work we provide a theoretical framework for structured prediction that generalizes the existing theory of surrogate methods for binary and multiclass classification based on estimating conditional probabilities with smooth convex surrogates (e.g. logistic regression). The theory relies on a natural characterization of structural properties of the task loss and allows to derive statistical guarantees for many widely used methods in the context of multilabeling, ranking, ordinal regression and graph matching. In particular, we characterize the smooth convex surrogates compatible with a given task loss in terms of a suitable Bregman divergence composed with a link function. This allows to derive tight bounds for the calibration function and to obtain novel results on existing surrogate frameworks for structured prediction such as conditional random fields and quadratic surrogates.

17 citations

Book ChapterDOI
20 Sep 2010
TL;DR: A new model family is proposed, Hidden Conditional Ordinal Random Fields (HCORFs), that explicitly models sequence dynamics as the dynamics of ordinal categories, and it is shown how classification of entire sequences can be formulated as an instance of learning and inference in H-CORFs.
Abstract: Conditional Random Fields and Hidden Conditional Random Fields are a staple of many sequence tagging and classification frameworks An underlying assumption in those models is that the state sequences (tags), observed or latent, take their values from a set of nominal categories These nominal categories typically indicate tag classes (eg, part-of-speech tags) or clusters of similar measurements However, in some sequence modeling settings it is more reasonable to assume that the tags indicate ordinal categories or ranks Dynamic envelopes of sequences such as emotions or movements often exhibit intensities growing from neutral, through raising, to peak values In this work we propose a new model family, Hidden Conditional Ordinal Random Fields (HCORFs), that explicitly models sequence dynamics as the dynamics of ordinal categories We formulate those models as generalizations of ordinal regressions to structured (here sequence) settings We show how classification of entire sequences can be formulated as an instance of learning and inference in H-CORFs In modeling the ordinal-scale latent variables, we incorporate recent binning-based strategy used for static ranking approaches, which leads to a log-nonlinear model that can be optimized by efficient quasi-Newton or stochastic gradient type searches We demonstrate improved prediction performance achieved by the proposed models in real video classification problems

17 citations

Journal ArticleDOI
TL;DR: Cross-sample entropy and inner composition alignment have been introduced as prominent methods to estimate the information interactions of complex systems to detect the interactions among the ordinal pattern of stock return and volatility series and to uncover the information exchanges across sectors in Chinese stock markets.
Abstract: The interactions among time series as individual components of complex systems can be quantified by measuring to what extent they exchange information among each other. In many applications, one focuses not on the original series but on its ordinal pattern. In such cases, trivial noises appear more likely to be filtered and the abrupt influence of extreme values can be weakened. Cross-sample entropy and inner composition alignment have been introduced as prominent methods to estimate the information interactions of complex systems. In this paper, we modify both methods to detect the interactions among the ordinal pattern of stock return and volatility series, and we try to uncover the information exchanges across sectors in Chinese stock markets.

17 citations


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