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Regression for ordinal variables without underlying continuous variables
Information Sciences, 2006Several techniques exist nowadays for continuous (i.e. numerical) data analysis and modeling. However, although part of the information gathered by companies, statistical offices and other institutions is numerical, a large part of it is represented using categorical variables in ordinal or nominal scales.
Vicenç Torra +3 more
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Latent Variable Models for Clustered Ordinal Data
Biometrics, 1995Existing methods for the analysis of clustered, ordinal data are inappropriate for certain applications. We propose latent variable models for clustered ordinal data which are derived as natural extensions of latent variable models for clustered binary data (Qu, Williams, Beck, and Medendorp, 1992. Biometrics 48, 1095-1102). These models can be applied
Qu, Yinsheng +2 more
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A Latent Variable Model for Ordinal Variables
Applied Psychological Measurement, 2000A full-information maximum likelihood method for fitting a multidimensional latent variable model to a set of ordinal observed variables is discussed. This method is an implementation of a general class of models for ordinal variables, and for regression models with one ordinal dependent variable and all explanatory variables observed.
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Regression Models with Ordinal Variables
American Sociological Review, 1984Most discussions of ordinal variables in the sociological literature debate the suitability of linear regression and structural equation methods when some variables are ordinal. Largely ignored in these discussions are methods for ordinal variables that are natural extensions of probit and logit models for dichotomous variables.
Christopher Winship, Robert D. Mare
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Asymmetry for ordinal variables
Statistica & Applicazioni : V, 2, 2007, 2007This paper proposes, for ordinal variables, and index of asymmetry based on the cumulative and retrocumulative frequencies. The paper shows that this new index has a connection with the bipolar mean, recently introduced by Maffenini and Zenga(2006).
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A PC algorithm variation for ordinal variables
Computational Statistics, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Statistical Models for Ordinal Variables.
Contemporary Sociology, 1995Mark von Tress +2 more
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2019
A commonly undervalued and mistreated type of outcome variable is the ordinal one. Two common types of mistreatment are treating ordinal variables as interval/ratio level outcome variables (frequently in linear models) and, in other cases, as multicategory nominal outcome variables. Multicategory nominal outcome variables are covered in Chap.
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A commonly undervalued and mistreated type of outcome variable is the ordinal one. Two common types of mistreatment are treating ordinal variables as interval/ratio level outcome variables (frequently in linear models) and, in other cases, as multicategory nominal outcome variables. Multicategory nominal outcome variables are covered in Chap.
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A Method for Transforming Ordinal Variables
2017The similarity of individuals with respect to a number of ordinal variables is the main topic of this work. We consider the application of Multiple Correspondence Analysis (MCA) on k ordinal variables for N subjects. In the context of ordinary MCA, each variable is transformed into a suitable number of binary variables and the derived matrix is ...
Odysseas Moschidis +1 more
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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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