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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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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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Correlations with ordinal data
Journal of Econometrics, 1974In most econometric analyses the data are uniquely defined except for a choice of units (e.g., physical quantities or value flows) and/or a location parameter (e.g., time). In some cases the cardinality of the data is less clear. For instance, building inspectors may rate various aspects of dwellings and neighborhoods on a one to five scale, the ...
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PENGOLAHAN DATA BERSKALA ORDINAL
Sigma-Mu, 1970Dalam analisis Multivariat, pengolahan data terkadang mengharuskan data berskala interval / metrik. Apabila data yang dihadapi berskala ordinal, sebaiknya digunakan analisis multivariat nonparametrik. Namun, analisis ini mempunyai banyak kesulitan. Karena software yang mampu mengakomodasi teknik-teknik seperti ini masih kurang.
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Markov Models for Repeated Ordinal Data
Methods of Information in Medicine, 2006Summary Objectives: To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. Methods: A brief review of regression methods for correlated data is given.
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Calculating reliability measures for ordinal data
British Journal of Clinical Psychology, 1986Establishing the reliability of measures taken by judges is important in both clinical and research work. Calculating the statistic of choice, the kappa coefficient, unfortunately is not a particularly quick and simple procedure. Two much‐needed practical tools have been developed to overcome these difficulties: a comprehensive and easily understood ...
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