Results 251 to 260 of about 179,170 (301)

Simulating Ordinal Data

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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Diagnostics for Ordinal Data

The problem we aim to address in this dissertation is to develop a more straightforward method for examining correlation-related diagnostics in ordinal data. Analyzing data that falls between numerical and categorical types can be challenging, particularly when it involves ordinal variables.
Nii-Ayitey, Justice
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Metric Learning for Ordinal Data

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
A large amount of ordinal-valued data exist in many domains, including medical and health science, social science, economics, political science, etc. Unlike image and speech datasets of real-valued data, learning with ordinal variables (i.e., features) presents unique challenges.
Yuan Shi, Wenzhe Li, Fei Sha
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Models for Ordinal Agreement Data

Biometrical Journal, 2001
Summary: 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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Answering Ordinal Questions with Ordinal Data Using Ordinal Statistics

Multivariate Behavioral Research, 1996
It 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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Consistency in ordinal data analysis I

Mathematical Social Sciences, 2002
The 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., 2005
It 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

2000
This 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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Inequality with Ordinal Data

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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Learning ordinal data

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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