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HierarchicalCUBModels for Ordinal Variables
Communications in Statistics - Theory and Methods, 2012Hierarchical CUB models are a generalization of CUB models in which parameters are allowed to be random. The main feature that distinguishes such proposal from the standard one is the modeling of variation among groups. We illustrate the usefulness of these hierarchical structures by discussing model specification, inferential issues, and empirical ...
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Mediation Analysis for Ordinal Outcome Variables
2015This study compared four methods with respect to three factors, namely sample size, size of mediating effects, and the number of categories of the outcome variable, as based on the work of MacKinnon, to analyze the mediation effects for ordinal outcome variables.
Hongyun Liu, Yunyun Zhang, Fang Luo
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Comparing classifiers for ordinal variables
2020To choose a single category of a qualitative variable using its predicted probability distribution is the final task to solve a classification problem. In this study, five predictive criteria are proposed and compared with the modal one, which is the standard criterion.
Silvia Golia, Maurizio Carpita
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Bipolar mean for ordinal variables
2005This paper proposes, for ordinal variables, a new type of mean called bipolar mean, that is a frequency distribution with the total size n concentrated on one category or on two consecutive categories. The bipolar mean is coherent with the usual statistics dominance that is based on the retro-cumulative frequencies.
MAFFENINI, WALTER, ZENGA, MICHELE
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Microbial diversity in extreme environments
Nature Reviews Microbiology, 2021Wen-Sheng Shu, Li-Nan Huang
exaly
Towards precision oncology with patient-derived xenografts
Nature Reviews Clinical Oncology, 2022Elena Grassi, Livio Trusolino
exaly
Metallurgy, mechanistic models and machine learning in metal printing
Nature Reviews Materials, 2020Tarasankar DebRoy +2 more
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