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Journal articles on the topic 'Ordinal methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

O'Connell, Ann Aileen. "Methods for Modeling Ordinal Outcome Variables." Measurement and Evaluation in Counseling and Development 33, no. 3 (2000): 170–93. http://dx.doi.org/10.1080/07481756.2000.12069008.

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2

Gutierrez, Pedro Antonio, Maria Perez-Ortiz, Javier Sanchez-Monedero, Francisco Fernandez-Navarro, and Cesar Hervas-Martinez. "Ordinal Regression Methods: Survey and Experimental Study." IEEE Transactions on Knowledge and Data Engineering 28, no. 1 (2016): 127–46. http://dx.doi.org/10.1109/tkde.2015.2457911.

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3

Lansdowne, Zachary F. "Ordinal ranking methods for multicriterion decision making." Naval Research Logistics 43, no. 5 (1996): 613–27. http://dx.doi.org/10.1002/(sici)1520-6750(199608)43:5<613::aid-nav2>3.0.co;2-8.

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4

Danielson, Mats, Love Ekenberg, and Ying He. "Augmenting Ordinal Methods of Attribute Weight Approximation." Decision Analysis 11, no. 1 (2014): 21–26. http://dx.doi.org/10.1287/deca.2013.0289.

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5

Greco, Salvatore, Miłosz Kadziński, Vincent Mousseau, and Roman Słowiński. "ELECTREGKMS: Robust ordinal regression for outranking methods." European Journal of Operational Research 214, no. 1 (2011): 118–35. http://dx.doi.org/10.1016/j.ejor.2011.03.045.

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6

Jakobsson, Ulf, and Albert Westergren. "Statistical methods for assessing agreement for ordinal data." Scandinavian Journal of Caring Sciences 19, no. 4 (2005): 427–31. http://dx.doi.org/10.1111/j.1471-6712.2005.00368.x.

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7

Cugnata, Federica, and Silvia Salini. "Comparison of alternative imputation methods for ordinal data." Communications in Statistics - Simulation and Computation 46, no. 1 (2014): 315–30. http://dx.doi.org/10.1080/03610918.2014.963611.

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8

Dopazo, Esther, and María L. Martínez-Céspedes. "Rank aggregation methods dealing with ordinal uncertain preferences." Expert Systems with Applications 78 (July 2017): 103–9. http://dx.doi.org/10.1016/j.eswa.2017.01.051.

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9

Casacci, Sara, and Adriano Pareto. "Methods for quantifying ordinal variables: a comparative study." Quality & Quantity 49, no. 5 (2014): 1859–72. http://dx.doi.org/10.1007/s11135-014-0063-2.

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10

Hedeker, Donald. "Methods for Multilevel Ordinal Data in Prevention Research." Prevention Science 16, no. 7 (2014): 997–1006. http://dx.doi.org/10.1007/s11121-014-0495-x.

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11

Sobíšek, Lukáš, and Hana Řezanková. "Comparison of Dimensionality Reduction Methods Applied to Ordinal Variables." Acta Oeconomica Pragensia 19, no. 1 (2011): 3–19. http://dx.doi.org/10.18267/j.aop.323.

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12

Pérez, J. "Theoretical elements of comparison among ordinal discrete multicriteria methods." Journal of Multi-Criteria Decision Analysis 3, no. 3 (1994): 157–76. http://dx.doi.org/10.1002/mcda.4020030303.

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13

Feldmann, Uwe, and Ingo Steudel. "Methods of ordinal classification applied to medical scoring systems." Statistics in Medicine 19, no. 4 (2000): 575–86. http://dx.doi.org/10.1002/(sici)1097-0258(20000229)19:4<575::aid-sim357>3.0.co;2-a.

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14

Ahn, Byeong Seok, and Kyung Sam Park. "Comparing methods for multiattribute decision making with ordinal weights." Computers & Operations Research 35, no. 5 (2008): 1660–70. http://dx.doi.org/10.1016/j.cor.2006.09.026.

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15

Avigad, Jeremy, and Richard Sommer. "The model-theoretic ordinal analysis of theories of predicative strength." Journal of Symbolic Logic 64, no. 1 (1999): 327–49. http://dx.doi.org/10.2307/2586768.

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AbstractWe use model-theoretic methods described in [3] to obtain ordinal analyses of a number of theories of first- and second-order arithmetic, whose proof-theoretic ordinals are less than or equal to Γ0.

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16

Gentry, Amanda Elswick, Colleen K. Jackson-Cook, Debra E. Lyon, and Kellie J. Archer. "Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces." Cancer Informatics 14s2 (January 2015): CIN.S17277. http://dx.doi.org/10.4137/cin.s17277.

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The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covari

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17

Jenkins, Stephen P. "Comparing distributions of ordinal data." Stata Journal: Promoting communications on statistics and Stata 20, no. 3 (2020): 505–31. http://dx.doi.org/10.1177/1536867x20953565.

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To compare distributions of ordinal data such as individuals’ responses on Likert-type scale variables summarizing subjective well-being, we should not apply the toolbox of methods developed for cardinal variables such as income. Instead, we should use an analogous toolbox that accounts for the ordinal nature of the responses. In this article, I review these methods and introduce a new command, ineqord, for undertaking distributional comparisons. As the empirical illustrations demonstrate, ineqord can be used for dominance checks as well as for estimation of indices of polarization and inequal

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18

Zonneveld, Thomas P., Annette Aigner, Rolf H. H. Groenwold, et al. "Confounding adjustment performance of ordinal analysis methods in stroke studies." PLOS ONE 15, no. 4 (2020): e0231670. http://dx.doi.org/10.1371/journal.pone.0231670.

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19

Gerlach, Robert W. "Measures of association for comparing analytical methods generating ordinal results." Journal of Chemometrics 12, no. 2 (1998): 105–23. http://dx.doi.org/10.1002/(sici)1099-128x(199803/04)12:2<105::aid-cem503>3.0.co;2-i.

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20

Hubbard, D., and D. Evans. "Problems with scoring methods and ordinal scales in risk assessment." IBM Journal of Research and Development 54, no. 3 (2010): 2:1–2:10. http://dx.doi.org/10.1147/jrd.2010.2042914.

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21

Lei, Pui-Wa. "Evaluating estimation methods for ordinal data in structural equation modeling." Quality and Quantity 43, no. 3 (2007): 495–507. http://dx.doi.org/10.1007/s11135-007-9133-z.

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22

Tang, Liansheng Larry, Ao Yuan, John Collins, Xuan Che, and Leighton Chan. "Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard." Cancer Informatics 16 (January 1, 2017): 117693511668606. http://dx.doi.org/10.1177/1176935116686063.

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The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input “data.” It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. The key difference between the proposed method and the method of Tang and Zhou lies in the response varia

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23

Fu, L. Y., Y. G. Wang, and C. J. Liu. "Rank Regression for Analyzing Ordinal Qualitative Data for Treatment Comparison." Phytopathology® 102, no. 11 (2012): 1064–70. http://dx.doi.org/10.1094/phyto-05-11-0128.

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Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with

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24

Wellmann, J. "Markov Models for Repeated Ordinal Data." Methods of Information in Medicine 45, no. 04 (2006): 414–18. http://dx.doi.org/10.1055/s-0038-1634097.

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Summary 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. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. Re

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25

SUHARJO, B., LA MBAU, and N. K. K. ARDANA. "PEMANTAUAN PERSAMAAN MODEL STRUKTURAL DALAM DATA ORDINAL." Journal of Mathematics and Its Applications 8, no. 1 (2009): 21. http://dx.doi.org/10.29244/jmap.8.1.21-36.

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Structural equation modeling (SEM) is one of multivariate techniques that can estimates a series of interrelated dependence relationships from a number of endogenous and exogenous variables, as well as latent (unobserved) variables simultaneously. To estimates their parameters, SEM based on structure covariance matrix, there are severals methods can be used as estimation methods, namely maximum likelihood (ML), weighted least squares (WLS), generalized least squares (GLS) and unweighted least squares (ULS). The purpose of this paper are to learn these methods in estimating SEM parameters and t

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26

Cruz-Reyes, Laura, Eduardo Fernandez, J. Patricia Sanchez-Solis, Carlos A. Coello Coello, and Claudia Gomez. "Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods." Swarm and Evolutionary Computation 54 (May 2020): 100652. http://dx.doi.org/10.1016/j.swevo.2020.100652.

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27

Biruk, Sławomir, and Piotr Jaśkowski. "Selection of subcontractors using ordinal ranking methods based on Condorcet approach." Budownictwo i Architektura 15, no. 4 (2016): 033–40. http://dx.doi.org/10.24358/bud-arch_16_154_04.

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A choice of a subcontractor may have critical impact on realization of the project, it has influence on the cost, duration, and quality. Selection of the best sucontractor can be defined as multiple criteria decision making problem (MCDM) of choosing a proper offer from set of alternatives evaluated by using set of criteria. Decision maker should determine the criteria as objective and measurable. Significance of decision making problem is presented by large amount of theories and methods developed for solving MCDM problems and number of criteria considered in these problems. A Condorcet metho

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28

Mateos, A., A. Jiménez-Martín, E. A. Aguayo, and P. Sabio. "Dominance intensity measuring methods in MCDM with ordinal relations regarding weights." Knowledge-Based Systems 70 (November 2014): 26–32. http://dx.doi.org/10.1016/j.knosys.2013.12.002.

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29

Gavião, Luiz Octávio, Fernando Toledo Ferraz, Gilson Brito Alves Lima, and Annibal Parracho Sant’Anna. "Assessment of the “Disrupt-O-Meter” model by ordinal multicriteria methods." RAI Revista de Administração e Inovação 13, no. 4 (2016): 305–14. http://dx.doi.org/10.1016/j.rai.2016.05.002.

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30

Jia, Fan, and Wei Wu. "Evaluating methods for handling missing ordinal data in structural equation modeling." Behavior Research Methods 51, no. 5 (2019): 2337–55. http://dx.doi.org/10.3758/s13428-018-1187-4.

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31

Gange, Stephen J., Kathryn L. P. Linton, Alastair J. Scott, David L. Demets, and Ronald Klein. "A comparison of methods for correlated ordinal measures with ophthalmic applications." Statistics in Medicine 14, no. 18 (1995): 1961–74. http://dx.doi.org/10.1002/sim.4780141803.

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32

ROSNER, BERNARD, and ROBERT J. GLYNN. "MULTIVARIATE METHODS FOR CLUSTERED ORDINAL DATA WITH APPLICATIONS TO SURVIVAL ANALYSIS." Statistics in Medicine 16, no. 4 (1997): 357–72. http://dx.doi.org/10.1002/(sici)1097-0258(19970228)16:4<357::aid-sim420>3.0.co;2-3.

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33

Sarnacchiaro, Pasquale, Antonello D’Ambra, and Luigi D’Ambra. "CATANOVA for ordinal variables using orthogonal polynomials with different scoring methods." Journal of Applied Statistics 43, no. 13 (2016): 2490–502. http://dx.doi.org/10.1080/02664763.2016.1184627.

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34

Ananth, C. "Regression models for ordinal responses: a review of methods and applications." International Journal of Epidemiology 26, no. 6 (1997): 1323–33. http://dx.doi.org/10.1093/ije/26.6.1323.

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35

Ma, Ke, Qianqian Xu, and Xiaochun Cao. "Robust Ordinal Embedding from Contaminated Relative Comparisons." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7908–15. http://dx.doi.org/10.1609/aaai.v33i01.33017908.

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Existing ordinal embedding methods usually follow a twostage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. However, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. In this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. The merits of our method are three-fold: (1) By virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) The proposed method is

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36

Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani. "Feature Selection for Ordinal Text Classification." Neural Computation 26, no. 3 (2014): 557–91. http://dx.doi.org/10.1162/neco_a_00558.

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Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other supervised learning tasks such as binary or multiclass classification, feature selection is often needed in order to improve efficiency and avoid overfitting. However, although feature selection has been exte

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37

König, J., and U. Feldmann. "Ordinal Classification in Medical Prognosis." Methods of Information in Medicine 41, no. 02 (2002): 154–59. http://dx.doi.org/10.1055/s-0038-1634300.

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Summary Objectives: Medical prognosis is commonly expressed in terms of ordered outcome categories. This paper provides simple statistical procedures to judge whether the predictor variables reflect this natural ordering. Methods: The concept of stochastic ordering in logistic regression and discrimination models is applied to naturally ordered outcome scales in medical prognosis. Results: The ordering stage is assessed by a data-generated choice between ordered, partially ordered, and unordered models. The ordinal structure of the outcome is particularly taken into consideration in the constr

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38

Spiessens, Bart, Emmanuel Lesaffre, Geert Verbeke, and KyungMann Kim. "Group Sequential Methods for an Ordinal Logistic Random-Effects Model Under Misspecification." Biometrics 58, no. 3 (2002): 569–75. http://dx.doi.org/10.1111/j.0006-341x.2002.00569.x.

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39

Fernández, Eduardo, José Rui Figueira, and Jorge Navarro. "Interval-based extensions of two outranking methods for multi-criteria ordinal classification." Omega 95 (September 2020): 102065. http://dx.doi.org/10.1016/j.omega.2019.05.001.

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40

Sholomov, L. A. "Logical Methods for Studying Relations in Criterial Spaces with Arbitrary Ordinal Scales." Automation and Remote Control 65, no. 5 (2004): 790–99. http://dx.doi.org/10.1023/b:aurc.0000028325.07505.2d.

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41

Donneau, A. F., M. Mauer, G. Molenberghs, and A. Albert. "A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data." Communications in Statistics - Simulation and Computation 44, no. 5 (2014): 1311–38. http://dx.doi.org/10.1080/03610918.2013.818690.

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42

Xu, Yejun, Qianqian Wang, Francisco Javier Cabrerizo, and Enrique Herrera-Viedma. "Methods to improve the ordinal and multiplicative consistency for reciprocal preference relations." Applied Soft Computing 67 (June 2018): 479–93. http://dx.doi.org/10.1016/j.asoc.2018.03.034.

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43

BROUWER, ROELOF K. "FUZZY CLUSTERING OF FEATURE VECTORS WITH SOME ORDINAL VALUED ATTRIBUTES USING GRADIENT DESCENT FOR LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (2008): 195–218. http://dx.doi.org/10.1142/s0218488508005133.

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There are well established methods for fuzzy clustering especially for the cases where the feature values are numerical of ratio or interval scale. Not so well established are methods to be applied when the feature values are ordinal or nominal. In that case there is no one best method it seems. This paper discusses a method where unknown numeric variables are assigned to the ordinal values. Part of minimizing an objective function for the clustering is to find numeric values for these variables. Thus real numbers of interval scale and even ratio scale for that matter are assigned to the origi

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44

Wang, Wei C., and Everarda G. Cunningham. "Comparison of Alternative Estimation Methods in Confirmatory Factor Analyses of the General Health Questionnaire." Psychological Reports 97, no. 1 (2005): 3–10. http://dx.doi.org/10.2466/pr0.97.1.3-10.

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This paper examines the implications of violating assumptions concerning the continuity and distributional properties of data in establishing measurement models in social science research. The General Health Questionnaire-12 uses an ordinal response scale. Responses to the GHQ-12 from 201 Hong Kong immigrants on arrival in Australia showed that the data were not normally distributed. A series of confirmatory factor analyses using either a Pearson product-moment or a polychoric correlation input matrix and employing either maximum likelihood, weighted least squares or diagonally weighted least

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45

Chen, Han-Ching, and Nae-Sheng Wang. "The Assignment of Scores Procedure for Ordinal Categorical Data." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/304213.

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Ordinal data are the most frequently encountered type of data in the social sciences. Many statistical methods can be used to process such data. One common method is to assign scores to the data, convert them into interval data, and further perform statistical analysis. There are several authors who have recently developed assigning score methods to assign scores to ordered categorical data. This paper proposes an approach that defines an assigning score system for an ordinal categorical variable based on underlying continuous latent distribution with interpretation by using three case study e

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46

Fang, Jian, Li Na Sui, and Hong Yi Jian. "Continuous Entropy Estimation with Different Unsupervised Discretization Methods." Applied Mechanics and Materials 380-384 (August 2013): 1617–20. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1617.

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In this paper, we compare and analyze the performances of nine unsupervised discretization methods, i.e., equal width, equal frequency, k-means clustering discretization, ordinal, fixed frequency, non-disjoint, proportional, weight proportional, mean value and standard deviation discretizations in the framework of continues entropy estimation based on 15 probability density distributions, i.e., Beta, Cauchy, Central Chi-Squared, Exponential, F, Gamma, Laplace, Logistic, Lognormal, Normal, Rayleigh, Student's-t, Triangular, Uniform, and Weibull distributions.

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47

Morris, Daryl E., Margaret Sullivan Pepe, and William E. Barlow. "Contrasting Two Frameworks for ROC Analysis of Ordinal Ratings." Medical Decision Making 30, no. 4 (2010): 484–98. http://dx.doi.org/10.1177/0272989x09357477.

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Background. Statistical evaluation of medical imaging tests used for diagnostic and prognostic purposes often employs receiver operating characteristic (ROC) curves. Two methods for ROC analysis are popular. The ordinal regression method is the standard approach used when evaluating tests with ordinal values. The direct ROC modeling method is a more recently developed approach, motivated by applications to tests with continuous values. Objective. The authors compare the methods in terms of model formulations, interpretations of estimated parameters, the ranges of scientific questions that can

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48

Sánchez-Monedero, J., Pedro A. Gutiérrez, Peter Tiňo, and C. Hervás-Martínez. "Exploitation of Pairwise Class Distances for Ordinal Classification." Neural Computation 25, no. 9 (2013): 2450–85. http://dx.doi.org/10.1162/neco_a_00478.

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Ordinal classification refers to classification problems in which the classes have a natural order imposed on them because of the nature of the concept studied. Some ordinal classification approaches perform a projection from the input space to one-dimensional (latent) space that is partitioned into a sequence of intervals (one for each class). Class identity of a novel input pattern is then decided based on the interval its projection falls into. This projection is trained only indirectly as part of the overall model fitting. As with any other latent model fitting, direct construction hints o

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49

Nunn, Aimie, Philip M. Bath, and Laura J. Gray. "Analysis of the Modified Rankin Scale in Randomised Controlled Trials of Acute Ischaemic Stroke: A Systematic Review." Stroke Research and Treatment 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/9482876.

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Background. Historically, most acute stroke clinical trials were neutral statistically, with trials typically dichotomising ordinal scales, such as the modified Rankin Scale. Studies published before 2007 have shown that preserving the ordinal nature of these scales increased statistical power. A systematic review of trials published since 2007 was conducted to reevaluate statistical methods used and to assess whether practice has changed.Methods. A search of electronic databases identified RCTs published between January 2007 and July 2014 in acute ischaemic stroke using an ordinal dependency

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50

Kim, Joungyoun, Sung-Cheol Yun, Johan Lim, Moo-Song Lee, Won Son, and Dohwan Park. "ROC Estimation from Clustered Data with an Application to Liver Cancer Data." Cancer Informatics 15s4 (January 2016): CIN.S40299. http://dx.doi.org/10.4137/cin.s40299.

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In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. We find that the proposed model defines a Lehmann family and can also introduce a location-scale family of a receiver op

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