Results 21 to 30 of about 474,403 (297)
Sparse Ordinal Logistic Regression and Its Application to Brain Decoding
Frontiers in Neuroinformatics, 2018 Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity.
Emi Satake +4 more
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Le tau et le tau-b de Kendall pour la corrélation de variables ordinales simples ou catégorielles [PDF]
Tutorials in Quantitative Methods for Psychology, 2009 Dans le langage de tous les jours, lexpression « corrélation entre deux variables » est entendue et bien comprise de manière générale : cest un lien, un rapport de correspondance grâce auquel la variation dun attribut peut être associée à la variation
Louis Laurencelle
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Barekeng, 2022 Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables.
Reni Amelia +2 more
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Multiple Ordinal Correlation Based on Kendall’s Tau Measure: A Proposal
Mathematics, 2021 The joint analysis of various ordinal variables is necessary in many experimental studies within research fields such as sociology and psychology. Therefore, the necessary measures of multiple ordinal dependence must be easy to interpret and facilitate ...
Juan M. Muñoz-Pichardo +3 more
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AXIOMATIC DETERMINATION OF A CLASS OF ORDINAL VARIATION MEASURES
Studies in Logic, Grammar and Rhetoric, 2017 The article deals with the problem of the dispersion of ordinal variables. At first, it specifies the very concept of dispersion for this type of scale. Then some of the most known measures that fit to the concept of ordinal variation are recalled.
Kęska Adam
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Bayesian test of independence and conditional independence of two ordinal variables [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 For analysis of contingency tables with large sample size, classical approaches using approximate methods have high power. However, when the sample size is small or some cells have frequencies less than 5, classical approaches are so conservative.
Zahra Saberi, Mojtab Ganjali
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Measuring risk with ordinal variables [PDF]
The Journal of Operational Risk, 2013 In this paper we propose a novel approach for measuring risks when the data available is expressed on an ordinal scale. As a result we obtain a new index of riskboundedbetween 0and 1,whichleadstoariskorderingthatisconsistentwith a stochastic dominance approach. The proposed measure, being nonparametric, can be applied to a wide range of problems, where
FIGINI, SILVIA, GIUDICI, PAOLO STEFANO
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Penalized Regression with Ordinal Predictors [PDF]
, 2008 Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this article penalized regression techniques are proposed.
Gertheiss, Jan, Tutz, Gerhard
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Adaptive Sparse Clustering of Mixed Data Using Azzalini-Encoded Ordinal Variables
AxiomsIn this paper, we propose a novel sparse clustering method designed for high-dimensional mixed-type data, integrating Azzalini’s score-based encoding for ordinal variables. Our approach aims to retain the inherent nature of each variable type—continuous,
Ismail Arjdal +3 more
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Iranian South Medical Journal, 2021 Background: Response variables in most medical and health-related research have an ordinal nature. Conventional modeling methods assume predictor variables to be independent, and consider a large number of samples (n) compared to the number of covariates
Zahra Torkashvand +3 more
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