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Multivariate Behavioral Research, 2012
The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required.
P.A. Ferrari, A. Barbiero
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The increasing use of ordinal variables in different fields has led to the introduction of new statistical methods for their analysis. The performance of these methods needs to be investigated under a number of experimental conditions. Procedures to simulate from ordinal variables are then required.
P.A. Ferrari, A. Barbiero
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Answering Ordinal Questions with Ordinal Data Using Ordinal Statistics
Multivariate Behavioral Research, 1996It is argued that ordinal statistical methods are often more appropriate than their more common counterparts for three types of reasons: Conclusions from them will be unaffected by monotonic transformation of the variables, they are statistically more robust when used appropriately, and they often correspond more closely to the goals of the ...
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Models for Ordinal Agreement Data
Biometrical Journal, 2001Summary: Statistical models can be used to describe the probabilistic structure underlying cross-classified agreement data. This article explains how models for ordinal agreement data can be understood in terms of an association component and an agreement component.
Schuster, Christof, von Eye, Alexander
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WIREs Computational Statistics, 2015
Classification is an important topic in statistical learning. The goal of classification is to build a predictive model from the training dataset for the class label of an observation. It is commonly assumed that the class labels are unordered. However, in many real applications, there exists an intrinsic ordinal relation between the class labels ...
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Classification is an important topic in statistical learning. The goal of classification is to build a predictive model from the training dataset for the class label of an observation. It is commonly assumed that the class labels are unordered. However, in many real applications, there exists an intrinsic ordinal relation between the class labels ...
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Randomized Designs: Ordinal Data, II
2016Chapter 6 utilizes the Multi-Response Permutation Procedures (MRPP) developed in Chap. 2 to establish relationships between the test statistics of MRPP, δ and \(\mathfrak{R}\), and multivariate generalizations of selected conventional tests and measures designed for the analysis of completely randomized data at the ordinal level of measurement ...
Kenneth J. Berry +2 more
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Economica, 2017
The standard theory of inequality measurement assumes that the equalisand is a cardinal quantity, with known cardinalization. However, one often needs to make inequality comparisons where either the cardinalization is unknown or the underlying data are categorical.
Cowell, Frank, Flachaire, Emmanuel
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The standard theory of inequality measurement assumes that the equalisand is a cardinal quantity, with known cardinalization. However, one often needs to make inequality comparisons where either the cardinalization is unknown or the underlying data are categorical.
Cowell, Frank, Flachaire, Emmanuel
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Ordinal Data: An Alternative Distribution
Psychometrika, 1979To date, virtually all techniques appropriate for ordinal data are based on the uniform probability distribution over the permutations. In this paper we introduce and examine an alternative probability model for the distribution of ordinal data. Preliminary to deriving the expectations of Spearman's rho and Kendall's tau under this model, we show how ...
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2007
The standard Data Envelopment Analysis (DEA) method requires that the values for all inputs and outputs are known exactly. When some inputs and output are imprecise data, such as interval or bounded data, ordinal data, and ratio bounded data, the resulting DEA model becomes a non-linear programming problem.
Yao Chen, Joe Zhu
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The standard Data Envelopment Analysis (DEA) method requires that the values for all inputs and outputs are known exactly. When some inputs and output are imprecise data, such as interval or bounded data, ordinal data, and ratio bounded data, the resulting DEA model becomes a non-linear programming problem.
Yao Chen, Joe Zhu
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ORDINAL REGRESSION MODELS FOR EPIDEMIOLOGIC DATA
American Journal of Epidemiology, 1989Health status is often measured in epidemiologic studies on an ordinal scale, but data of this type are generally reduced for analysis to a single dichotomy. Several statistical models have been developed to make full use of information in ordinal response data, but have not been much used in analyzing epidemiologic studies.
B G, Armstrong, M, Sloan
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1991
Convexity is a leading idea in data analysis, although it is mostly involved on an informal level; in particular, convexity in ordinal data has not been elaborated as a well defined tool. This paper presents a first discussion of convexity definitions in connection with examples of ordinal data.
Selma Strahringer, Rudolf Wille
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Convexity is a leading idea in data analysis, although it is mostly involved on an informal level; in particular, convexity in ordinal data has not been elaborated as a well defined tool. This paper presents a first discussion of convexity definitions in connection with examples of ordinal data.
Selma Strahringer, Rudolf Wille
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