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, 2010 In the recent years, a great interest has been devoted by researchers to categorical data and the related statistical methods employed for their joint analysis. Specifically in explorative analysis, the robustness and performance of these techniques can be assessed almost exclusively through simulation studies, which require to generate a huge number ...
P.A. Ferrari, A. Barbiero
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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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Journal of Water and Health, 2022 A case study of ordinal data from human organoleptic examination (sensory analysis) of drinking water obtained in an interlaboratory comparison of 49 ecological laboratories is described.
Tamar Gadrich +6 more
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Stats, 2022 Background: Data with ordinal categories occur in many diverse areas, but methodologies for modeling ordinal data lag severely behind equivalent methodologies for continuous data.
Daniel Fernández +4 more
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Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression. [PDF]
G3 (Bethesda), 2015 Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model developed for ordered categorical phenotypes. In statistical applications, due to the easy
Montesinos-López OA +4 more
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Qualitative ordinal scales: the concept of ordinal range [PDF]
, 2004 Many practical problems of quality control involve the use of ordinal scales. Questionnaires planned to collect judgments on qualitative or linguistic scales, whose levels are terms such as "good," "bad," "medium," etc., are extensively used both in ...
Franceschini, Fiorenzo +6 more
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Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021 Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on ...
I Ketut Adi Wirayasa +3 more
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Revstat Statistical Journal, 2011 For the analysis of square contingency tables, Tomizawa, Miyamoto and Ashihara (2003) considered a measure to represent the degree of departure from marginal homogeneity.
Kouji Yamamoto +2 more
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Inequality Comparisons with Ordinal Data [PDF]
Review of Income and Wealth, 2019 Non‐intersection of appropriately defined Generalized Lorenz (GL) curves is equivalent to a unanimous ranking of distributions of ordinal data by all Cowell and Flachaire (Economica, 2017) indices of inequality and by a new index based on GL curve areas.
Stone Center on Socio-Economic Inequality +1 more
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Ordinal Ridge Regression with Categorical Predictors [PDF]
, 2011 In multi-category response models categories are often ordered. In case of ordinal response models, the usual likelihood approach becomes unstable with ill-conditioned predictor space or when the number of parameters to be estimated is large relative to ...
Zahid, Faisal Maqbool
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