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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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Consistency in ordinal data analysis I
Mathematical Social Sciences, 2002The aim of this paper to solve the problem of characterizing independently of any particular field of data analysis, i.e., cluster analysis, factor analysis etc., reduction methods that satisfy the modest reduction requirement in case that the data are measured in a scale that is not necessarily rational.
Gerhard Herden, Andreas Pallack
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Mining Ordinal Patterns For Data Cleaning
Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004., 2005It is well recognized that sequential pattern mining plays an essential role in many scientific and business domains. In this paper, a new extension of sequential pattern, ordinal pattern, is proposed. An ordinal pattern is an ordinal sequence of attributes, whose values commonly occur in ascending order over data set.
Ya-Bo Liu, Dayou Liu
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On the Contextual Logic of Ordinal Data
2000This paper reports on first attempts to develop a contextual logic of ordinal data. The investigations are based on a mathematical theory of ordinal contexts which has been developed within Formal Concept Analysis. From ordinal contexts, binary power context families are derived as semantic basis of a contextual logic of ordinal data.
Silke Pollandt, Rudolf Wille
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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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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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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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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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The Ordination of Incidence Data
The Journal of Ecology, 1978SUMMARY (1) Principal components analysis of incidence (presence and absence) data produces a horseshoe effect. A new method, called step-across, is described which removes this effect. (2) From a matrix of joint occurrences, distances are found directly for all positive values.
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