Ordinal regression

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

A measurement-error model for binary and ordinal regression.

Abstract: Exposure assessment poses special problems in air pollution epidemiology. This paper proposes a probit regression model for binary and ordinal outcomes that uses exposure validation information to develop estimates for the coefficient of the true exposure when only the inaccurate ‘surrogate’ measure of exposure is available for the individuals in the health study. This method is closely related to recently developed measurement-error methods, and is based on the assumption that the outcome and the surrogate exposure are conditionally independent given the true exposure. A test statistic is proposed for checking this conditional independence assumption when more than one surrogate is available, and an interpretation of the coefficient estimate is provided in the event that the assumption is violated. The methods are applied to an example involving nitrogen dioxide exposure and wheeze in children.

Tests for goodness of fit in ordinal logistic regression models

Abstract: We examine three approaches for testing goodness of fit in ordinal logistic regression models: an ordinal version of the Hosmer–Lemeshow test (Cg), the Lipsitz test, and the Pulkstenis–Robinson (PR) tests. The properties of these tests have previously been investigated for the proportional odds model. Here, we extend the tests to two other commonly used models: the adjacent-category and the constrained continuation-ratio models. We use a simulation study to assess null distributions and power. All three tests work well and can detect several types of lack of fit under both the adjacent-category and constrained continuation-ratio models. The Cg and Lipsitz tests are best suited to detect lack of fit associated with continuous covariates, whereas the PR tests excel at detecting lack of fit associated with categorical covariates. We illustrate the use of the tests with data from a study of aftercare placement of psychiatrically hospitalized adolescents. Based on the results here and previous research...

MUSA-INT: Multicriteria customer satisfaction analysis with interacting criteria

Abstract: We are considering the problem of measuring and analyzing customer satisfaction concerning a product or a service evaluated on multiple criteria. The proposed methodology generalizes the MUSA (MUlticriteria Satisfaction Analysis) method. MUSA is a preference disaggregation method that, following the principle of ordinal regression analysis, finds an additive utility function representing both the comprehensive satisfaction level of a set of customers and a marginal satisfaction level with respect to each criterion. Differently from MUSA, the proposed approach, that we will call MUSA-INT, takes also into account positive and negative interactions among criteria, similarly to the multicriteria method UTA GMS -INT. Our method accepts evaluations on criteria with different ordinal scales which do not need to be transformed into a unique cardinal scale prior to the analysis. Moreover, instead of a single utility function, MUSA-INT can also take into account a set of utility functions representing customers' satisfaction, adopting the robust ordinal regression methodology. An illustrative example shows how the proposed methodology can be applied on a customers’ survey.

Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media.

Abstract: In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In particular, we propose a multi-task ordinal regression framework that models the two problems jointly. This is motivated by the observation that hyper-partisanship is often linked to low trustworthiness, e.g., appealing to emotions rather than sticking to the facts, while center media tend to be generally more impartial and trustworthy. We further use several auxiliary tasks, modeling centrality, hyper-partisanship, as well as left-vs.-right bias on a coarse-grained scale. The evaluation results show sizable performance gains by the joint models over models that target the problems in isolation.

L 1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets

Abstract: Health status and outcomes are frequently measured on an ordinal scale. For high-throughput genomic datasets, the common approach to analyzing ordinal response data has been to break the problem into one or more dichotomous response analyses. This dichotomous response approach does not make use of all available data and therefore leads to loss of power and increases the number of Type I errors. Herein we describe an innovative frequentist approach that combines two statistical techniques, L1 penalization and continuation ratio models, for modeling an ordinal response using gene expression microarray data. A simulation study was conducted to assess the performance of two computational approaches and two model selection criterion for fitting frequentist L1 penalized continuation ratio models. Moreover, the approaches were empirically compared using three application datasets, each of which seeks to classify an ordinal class using microarray gene expression data as the predictor variables. We conclude that the L1 penalized constrained continuation ratio model is a useful approach for modeling an ordinal response for datasets where the number of covariates (p) exceeds the sample size (n), and the decision of whether to use AIC or BIC for selecting the final model should depend upon the similarities between the pathologies underlying the disease states to be classified.