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Relevant bibliographies by topics / Ordinal methods

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Academic literature on the topic 'Ordinal methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Ordinal methods"

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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Ordinal methods"

1

Lo, Meng-Ting. "Alternative Methods for Modeling Clustered Ordinal Data." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586792558628762.

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Yang, Yishen. "On Rank-invariant Methods for Ordinal Data." Doctoral thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-53675.

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Data from rating scale assessments have rank-invariant properties only, which means that the data represent an ordering, but lack of standardized magnitude, inter-categorical distances, and linearity. Even though the judgments often are coded by natural numbers they are not really metric. The aim of this thesis is to further develop the nonparametric rank-based Svensson methods for paired ordinal data that are based on the rank-invariant properties only. The thesis consists of five papers. In Paper I the asymptotic properties of the measure of systematic disagreement in paired ordinal data, th

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Mathieson, Mark James. "Ordinal models and predictive methods in pattern recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299155.

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Neary, Dominic Mark. "Methods of analysis for ordinal repeated measures data." Thesis, University of Reading, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339521.

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Sanders, Margaret. "Multifactor Models of Ordinal Data: Comparing Four Factor Analytical Methods." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1388745127.

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Jin, Qianying. "Ordinal classification with non-parametric frontier methods : overview and new proposals." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1A007.

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Suivant l'idée de séparer deux groupes par une hypersurface, la frontière convexe (C) générée par la méthode d'analyse de l'enveloppe des données (DEA) est utilisée pour la séparation dans la classification. Aucune hypothèse sur la forme de l'hypersurface n'est nécessaire si l'on utilise une frontière DEA. De plus, son raisonnement sur l'appartenance est très clair en se référant à une observation de référence. Malgré ces points forts, le classificateur basé sur la frontière DEA n'est pas toujours performant dans la classification. Par conséquent, cette thèse vise à modifier les classificateur

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Ferber, Kyle L. "Methods for Predicting an Ordinal Response with High-Throughput Genomic Data." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4585.

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Multigenic diagnostic and prognostic tools can be derived for ordinal clinical outcomes using data from high-throughput genomic experiments. A challenge in this setting is that the number of predictors is much greater than the sample size, so traditional ordinal response modeling techniques must be exchanged for more specialized approaches. Existing methods perform well on some datasets, but there is room for improvement in terms of variable selection and predictive accuracy. Therefore, we extended an impressive binary response modeling technique, Feature Augmentation via Nonparametrics and Se

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Hou, Jiayi. "Regularization Methods for Predicting an Ordinal Response using Longitudinal High-dimensional Genomic Data." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3242.

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Ordinal scales are commonly used to measure health status and disease related outcomes in hospital settings as well as in translational medical research. Notable examples include cancer staging, which is a five-category ordinal scale indicating tumor size, node involvement, and likelihood of metastasizing. Glasgow Coma Scale (GCS), which gives a reliable and objective assessment of conscious status of a patient, is an ordinal scaled measure. In addition, repeated measurements are common in clinical practice for tracking and monitoring the progression of complex diseases. Classical ordinal mode

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Guo, Lei. "Bayesian Biclustering on Discrete Data: Variable Selection Methods." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11201.

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Biclustering is a technique for clustering rows and columns of a data matrix simultaneously. Over the past few years, we have seen its applications in biology-related fields, as well as in many data mining projects. As opposed to classical clustering methods, biclustering groups objects that are similar only on a subset of variables. Many biclustering algorithms on continuous data have emerged over the last decade. In this dissertation, we will focus on two Bayesian biclustering algorithms we developed for discrete data, more specifically categorical data and ordinal data.<br>Statistics

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Denbleyker, John Nickolas. "Comparing trend and gap statistics across tests: distributional change using ordinal methods and bayesian inference." Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2856.

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The shortcomings of the proportion above cut (PAC) statistic used so prominently in the educational landscape renders it a very problematic measure for making correct inferences with student test data. The limitations of PAC-based statistics are more pronounced with cross-test comparisons due to their dependency on cut-score locations. A better alternative is using mean-based statistics that can translate to parametric effect-size measures. However, these statistics as well can be problematic. When Gaussian assumptions are

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Books on the topic "Ordinal methods"

1

Ordinal methods for behavioral data analysis. Erlbaum, 1996.

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1953-, Albert Jim, ed. Ordinal data modeling. Springer, 1999.

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A, Keats J., ed. Ordinal measurement in the behavioral sciences. Lawrence Erlbaum Associates, 2003.

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M, Voloshchenko A., and Germogenova T. A, eds. Metody diskretnykh ordinat v zadachakh o perenose izluchenii͡a︡. In-t prikladnoĭ matematiki im. M.V. Keldysha Akademii nauk SSSR, 1986.

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Ang, W. T. Ordinary differential equations: Methods and applications. Universal Publishers, 2008.

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Butcher, J. C. Numerical Methods for Ordinary Differential Equations. John Wiley & Sons, Ltd., 2008.

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Kuzʹmina, R. P. Asymptotic methods for ordinary differential equations. Kluwer Academic Publishers, 2000.

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Gear, C. William. Parallel methods for ordinary differential equations. Department of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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Butcher, J. C. Numerical methods for ordinary differential equations. J. Wiley, 2002.

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Butcher, J. C. Numerical Methods for Ordinary Differential Equations. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119121534.

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Book chapters on the topic "Ordinal methods"

1

Pomerol, Jean-Charles, and Sergio Barba-Romero. "Ordinal Multicriterion Methods." In Multicriterion Decision in Management. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4459-3_5.

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Berry, Kenneth J., Paul W. Mielke, and Janis E. Johnston. "Randomized Block Designs: Ordinal Data." In Permutation Statistical Methods. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28770-6_10.

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Berry, Kenneth J., Paul W. Mielke, and Janis E. Johnston. "Randomized Designs: Ordinal Data, I." In Permutation Statistical Methods. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28770-6_5.

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Berry, Kenneth J., Paul W. Mielke, and Janis E. Johnston. "Randomized Designs: Ordinal Data, II." In Permutation Statistical Methods. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28770-6_6.

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Gaudette, Lisa, and Nathalie Japkowicz. "Evaluation Methods for Ordinal Classification." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01818-3_25.

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Fitzmaurice, Garrett M., and Stuart R. Lipsitz. "Binary and Ordinal Outcomes." In Handbook of Statistical Methods for Randomized Controlled Trials. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315119694-2.

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Hoff, Peter D. "Latent variable methods for ordinal data." In Springer Texts in Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92407-6_12.

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Cliff, Norman. "Ordinal methods in the assessment of change." In Best methods for the analysis of change: Recent advances, unanswered questions, future directions. American Psychological Association, 1991. http://dx.doi.org/10.1037/10099-003.

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Rassel, Gary, Suzanne Leland, Zachary Mohr, and Elizabethann O’Sullivan. "Examining Relationships Between Nominal and Ordinal Variables." In Research Methods for Public Administrators. Routledge, 2020. http://dx.doi.org/10.4324/9780429319860-13.

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Manolios, Panagiotis, and Daron Vroon. "Integrating Reasoning About Ordinal Arithmetic into ACL2." In Formal Methods in Computer-Aided Design. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30494-4_7.

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Conference papers on the topic "Ordinal methods"

1

Raman, Karthik, and Thorsten Joachims. "Methods for ordinal peer grading." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623654.

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Costa, João, and Jaime S. Cardoso. "oAdaBoost - An AdaBoost Variant for Ordinal Classification." In International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005191600680076.

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Huo, Zengwei, and Xin Geng. "Ordinal Zero-Shot Learning." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/266.

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Zero-shot learning predicts new class even if no training data is available for that class. The solution to conventional zero-shot learning usually depends on side information such as attribute or text corpora. But these side information is not easy to obtain or use. Fortunately in many classification tasks, the class labels are ordered, and therefore closely related to each other. This paper deals with zero-shot learning for ordinal classification. The key idea is using label relevance to expand supervision information from seen labels to unseen labels. The proposed method SIDL generates a su

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Podlipnig, S. "Applying ordinal time series methods to grid workload traces." In amp; Simulation of Computer and Telecommunication Systems (MASCOTS). IEEE, 2009. http://dx.doi.org/10.1109/mascot.2009.5367039.

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Azizi, Majid, Alireza Khakifirooz, and Farimah Moghimi. "Experience-Based Decision Support Methods Using Ordinal Expert Estimates." In The International Symposium on the Analytic Hierarchy Process. Creative Decisions Foundation, 2009. http://dx.doi.org/10.13033/isahp.y2009.047.

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Subramanian, Shivashankar, Trevor Cohn, and Timothy Baldwin. "Deep Ordinal Regression for Pledge Specificity Prediction." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1182.

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Da San Martino, Giovanni, Wei Gao, and Fabrizio Sebastiani. "QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification." In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/s16-1006.

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Liu, Yanzhu, Adams Wai Kin Kong, and Chi Keong Goh. "Deep Ordinal Regression Based on Data Relationship for Small Datasets." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/330.

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Ordinal regression aims to classify instances into ordinal categories. As with other supervised learning problems, learning an effective deep ordinal model from a small dataset is challenging. This paper proposes a new approach which transforms the ordinal regression problem to binary classification problems and uses triplets with instances from different categories to train deep neural networks such that high-level features describing their ordinal relationship can be extracted automatically. In the testing phase, triplets are formed by a testing instance and other instances with known ranks.

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Ishii, K., C. H. Lee, and R. A. Miller. "Methods for Process Selection in Design." In ASME 1990 Design Technical Conferences. American Society of Mechanical Engineers, 1990. http://dx.doi.org/10.1115/detc1990-0115.

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Abstract This paper describes our proposed methodology for process selection that applies to the early stages of product design. We focus on net-shape manufacturing processes and identify the major factors that affect the selection of an appropriate process. The sequence at which designers typically make decisions depends largely on the nature of the product and the development environment. Thus, a versatile methodology should consider all the factors simultaneously in assessing the suitability of the candidate processes. The paper describes three types of knowledge that represent the compatib

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"EVALUATION OF METHODS FOR CONVERTING REQUEST FOR QUOTATION DATA INTO ORDINAL PREFERENCE DATA - Estimating Product Preference in Online Shopping System." In 7th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0002545200240031.

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Reports on the topic "Ordinal methods"

1

Juang, Fen-Lien. Waveform methods for ordinary differential equations. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5005850.

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Dutt, Alok, Leslie Greengard, and Vladimir Rokhlin. Spectral Deferred Correction Methods for Ordinary Differential Equations. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada337779.

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Werner, L., and F. Odeh. Numerical Methods for Stiff Ordinary and Elliptic Partial Differential Equations. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada153247.

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Van Wieren, Jack. Using Diagonally Implicit Multistage Integration Methods for Solving Ordinary Differential Equations. Part 2: Implicit Methods. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328947.

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VanWieren, Jack. Using Diagonally Implicit Multistage Integration Methods for Solving Ordinary Differential Equations. Part 1: Introduction and Explicit Methods. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada324085.

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Chang, B. Analytical Tests for Ray Effect Errors in Discrete Ordinate Methods for Solving the Neutron Transport Equation. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15014046.

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Hamill, Daniel, and Gabrielle David. Hydrologic analysis of field delineated ordinary high water marks for rivers and streams. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41681.

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Streamflow influences the distribution and organization of high water marks along rivers and streams in a landscape. The federal definition of ordinary high water mark (OHWM) is defined by physical and vegetative field indicators that are used to identify inundation extents of ordinary high water levels without any reference to the relationship between streamflow and regulatory definition. Streamflow is the amount, or volume, of water that moves through a stream per unit time. This study explores regional characteristics and relationships between field-delineated OHWMs and frequency-magnitude

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Ripey, Mariya. NUMBERS IN THE NEWS TEXT (BASED ON MATERIAL OF ONE ISSUE OF NATIONWIDE NEWSPAPER “DAY”). Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11106.

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The article is devoted to the analysis of the digital content of publications of one issue of the daily All-Ukrainian newspaper “Den” (March 13-14, 2020). The author aims to identify the main thematic groups of digital designations, as well as to consider cases of justified and unsuccessful use of digital designations. Applying the content analysis method, the author identifies publications that contain numerical notations, determines the number of such notations and their affiliation with the main subject groups. Finds that the thematic group of digital designations “time” (58.6% of all digit

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Venäläinen, Ari, Sanna Luhtala, Mikko Laapas, et al. Sää- ja ilmastotiedot sekä uudet palvelut auttavat metsäbiotaloutta sopeutumaan ilmastonmuutokseen. Finnish Meteorological Institute, 2021. http://dx.doi.org/10.35614/isbn.9789523361317.

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Climate change will increase weather induced risks to forests, and thus effective adaptation measures are needed. In Säätyö project funded by the Ministry of Agriculture and Forestry, we have summarized the data that facilitate adaptation measures, developed weather and climate services that benefit forestry, and mapped what kind of new weather and climate services are needed in forestry. In addition, we have recorded key further development needs to promote adaptation. The Säätyö project developed a service product describing the harvesting conditions of trees based on the soil moisture asses

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