We investigate the problem of predicting variables of ordinal scale. This taks is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is ba...

OBJECTIVES To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. METHODS Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese...

BACKGROUND AND PURPOSE Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. METHODS Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of inte...

In this paper we deal with an urban and territorial planning problem by applying the Non Additive Robust Ordinal Regression (NAROR). NAROR is a recent extension of the Robust Ordinal Regression (ROR) family of Multiple Criteria Decision Aiding (MCDA) methods to the Choquet integral preference model which permits to represent interaction between considered criteria through the use of a set of no...

The random forest method is a commonly used tool for classification with high-dimensional data that is able to rank candidate predictors through its inbuilt variable importance measures (VIMs). It can be applied to various kinds of regression problems including nominal, metric and survival response variables. While classification and regression problems using random forest methodology have been...

Background and Purpose—Most large acute stroke trials have been neutral. Functional outcome is usually analyzed using a yes or no answer, eg, death or dependency versus independence. We assessed which statistical approaches are most efficient in analyzing outcomes from stroke trials. Methods—Individual patient data from acute, rehabilitation and stroke unit trials studying the effects of interv...

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 t...

Iris recognition provides a reliable method for personal identification. Inspired by recent achievements in the field of visual neuroscience, we encode the non-local image comparisons qualitatively for iris recognition. In this scheme, each bit iris code corresponds to the sign of an inequality across several distant image regions. Compared with local ordinal measures, the relationships of diss...

Social scientists, particularly political scientists, frequently use ordinal survey items as dependent variables in models of political attitudes. Commonly, normal-theory modeling strategies like ordinary least squares regression are applied to these items. Additionally, workers also make frequent use of the proportional odds (ordinal logit) model or cumulative probit model when working with su...

We introduce a robust regression method for imprecise data, and apply it to social survey data. Our method combines nonparametric likelihood inference with imprecise probability, so that only very weak assumptions are needed and different kinds of uncertainty can be taken into account. The proposed regression method is based on interval dominance: interval estimates of quantiles of the error di...