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Journal articles on the topic 'Ranking data'

Author: Grafiati

Published: 1 July 2021

Last updated: 29 July 2025

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1

Banihashemi, Sayyid Ali, and Mohammad Khalilzadeh. "A new approach for ranking efficient DMUs with data envelopment analysis." World Journal of Engineering 17, no. 4 (2020): 573–83. http://dx.doi.org/10.1108/wje-03-2020-0092.

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Purpose Classical models of data envelopment analysis (DEA) calculate the efficiency of decision-making units do not differentiate between efficient units. The purpose of this paper is to present a new method for ranking efficient units and compare it with the other methods presented in this field. Design/methodology/approach In this paper, a new method is presented for ranking efficient units. To validate the proposed method, a real case, which was studied by Li et al. (2016) is examined and the rankings of the efficient units are compared with four other methods including the Andersen and Pe

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Ivanov, A. A., and N. P. Yashina. "Big Data Analysis in Multi-Criteria Choice Problems." Моделирование и анализ данных 12, no. 2 (2022): 5–19. http://dx.doi.org/10.17759/mda.2022120201.

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The problem of multi-criteria choice with non-uniform scales of criteria is considered. A model of a multicriteria choice problem is described, the main elements of which are sets of alternatives and quality criteria, as well as algorithms that allow ranking alternatives without prior reduction of the criteria scales to homogeneous ones. Algorithms for constructing aggregated ranking of alternatives are based on the construction of preference matrices by criteria containing information on the degree of superiority of one alternative over another. Propositions are proved that allow ranking alte

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Zar, Jerrold H. "Ranking data with BASIC." Behavior Research Methods, Instruments, & Computers 17, no. 1 (1985): 142. http://dx.doi.org/10.3758/bf03200918.

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Jestes, Jeffrey, Jeff M. Phillips, Feifei Li, and Mingwang Tang. "Ranking large temporal data." Proceedings of the VLDB Endowment 5, no. 11 (2012): 1412–23. http://dx.doi.org/10.14778/2350229.2350257.

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5

Grossman, J. P., and Gregory Minton. "Inversions in ranking data." Discrete Mathematics 309, no. 20 (2009): 6149–51. http://dx.doi.org/10.1016/j.disc.2009.04.030.

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Doğan, Güleda, and Umut Al. "Is it possible to rank universities using fewer indicators? A study on five international university rankings." Aslib Journal of Information Management 71, no. 1 (2019): 18–37. http://dx.doi.org/10.1108/ajim-05-2018-0118.

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Purpose The purpose of this paper is to analyze the similarity of intra-indicators used in research-focused international university rankings (Academic Ranking of World Universities (ARWU), NTU, University Ranking by Academic Performance (URAP), Quacquarelli Symonds (QS) and Round University Ranking (RUR)) over years, and show the effect of similar indicators on overall rankings for 2015. The research questions addressed in this study in accordance with these purposes are as follows: At what level are the intra-indicators used in international university rankings similar? Is it possible to gro

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Wang, Jingyan, and Nihar B. Shah. "Ranking and Rating Rankings and Ratings." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (2020): 13704–7. http://dx.doi.org/10.1609/aaai.v34i09.7126.

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Cardinal scores collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are typically far more complex and not well understood. It is widely believed that in the absence of simplifying assumptions on the miscalibration, the only useful information in practice from the cardinal scores is the induced ranking. In this paper we address the fundamental question of whether this

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Rashid, Mahbub. "DesignIntelligence and the Ranking of Professional Architecture Programs: Issues, Impacts, and Suggestions." Architecture 2, no. 3 (2022): 593–615. http://dx.doi.org/10.3390/architecture2030032.

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This paper studies the annual rankings of professional architectural degree programs by DesignIntelligence (DI). It uses a literature review and the statistical analysis of DI rankings and program-specific data to explore the limitations of the ranking system and its impacts on programs and public opinion. According to the findings of the study, the limitations of this system are related to the data it uses, the methods it uses to collect the data, and the way it uses the data for ranking purposes. Still, the ranking system can force architectural programs into a costly campaign for better ran

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Candilasa, Jahzeel M., and Kent T. Onahon. "Global University Rankings: Characterization of Higher Education, Institution’s Competitiveness." International Journal of Research and Innovation in Social Science VIII, no. XII (2025): 3267–79. https://doi.org/10.47772/ijriss.2024.8120270.

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Global university rankings serve as information tool for students in choosing their studies, whereas institutional leaders regard them as a management and strategic decision-making tool. However, little is known about the relevance of global university rankings for developing countries like the Philippines. Using cluster analysis and stepwise regression, this study investigated the competitiveness indicators among clusters of top-ranking universities from three different global university-ranking organizations namely. Furthermore, it also created a best-fit model that accurately predicts insti

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Biolcati Rinaldi, Ferruccio, Daniele Checchi, Chiara Guglielmetti, Silvia Salini, and Matteo Turri. "Ranking e valutazione: il caso delle classifiche delle universitŕ." RIV Rassegna Italiana di Valutazione, no. 41 (May 2009): 81–114. http://dx.doi.org/10.3280/riv2008-041006.

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- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such

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Alvo, Mayer, and Kadir Ertas. "Graphical methods for ranking data." Canadian Journal of Statistics 20, no. 4 (1992): 469–82. http://dx.doi.org/10.2307/3315616.

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12

Brady, Henry E. "Dimensional Analysis of Ranking Data." American Journal of Political Science 34, no. 4 (1990): 1017. http://dx.doi.org/10.2307/2111470.

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Poon, Wai-Yin, and Wai Chan. "Influence analysis of ranking data." Psychometrika 67, no. 3 (2002): 421–36. http://dx.doi.org/10.1007/bf02294994.

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Franz, Thomas, and Steffen Staab. "Ranking und Clustering Linked Data." Datenbank-Spektrum 10, no. 2 (2010): 67–72. http://dx.doi.org/10.1007/s13222-010-0016-4.

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Bacon-Shone, John. "Ranking Methods for Compositional Data." Applied Statistics 41, no. 3 (1992): 533. http://dx.doi.org/10.2307/2348087.

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Kundu, Anirban, Guanxiong Xu, and Chunlin Ji. "Data Specific Ranking in Cloud." International Journal of Cloud Applications and Computing 4, no. 4 (2014): 32–41. http://dx.doi.org/10.4018/ijcac.2014100103.

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In this paper, the major goal is to achieve searching in distributed network with proper ranking. Heterogeneous framework is going to be utilized using existing machines and high-end workstations. Data redundancy is responsible for enhancing dynamic scheduling in run-time. Dynamic scheduling is going to be exploited determining possible sub-network and related servers for specific user targeted tasks using active and busy ranking status of servers. Minimization of time and maximization of speed are objectives of proposed system structure.

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Espejo, Mariano Ruiz. "Statistical Methods for Ranking Data." International Statistical Review 83, no. 1 (2015): 172–73. http://dx.doi.org/10.1111/insr.12095_11.

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Hua, Ming, Jian Pei, and Xuemin Lin. "Ranking queries on uncertain data." VLDB Journal 20, no. 1 (2010): 129–53. http://dx.doi.org/10.1007/s00778-010-0196-4.

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Critchlow, Douglas E., and Joseph S. Verducci. "An Omnibus Test for Systematic Changes in Judges’ Rankings." Journal of Educational Statistics 17, no. 1 (1992): 1–26. http://dx.doi.org/10.3102/10769986017001001.

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Paired rankings arise when each subject in a study independently ranks a set of items, undergoes a treatment, and afterwards ranks the same set of items. For such data, a statistical test is proposed to detect if the subjects’ posttreatment rankings have moved systematically toward some unknown ranking or set of rankings. The null hypothesis for this test is that each subject’s post-treatment ranking is symmetrically distributed about his pretreatment ranking. The exact and asymptotic null distributions of the test statistic are simulated and compared, and the power of the test is studied. Usi

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Cramer, Kenneth M., and Stewart Page. "Cluster Analysis and Rankings of Canadian Universities: Misadventures with Rank-based Data and Implications for the Welfare of Students." Applied Multivariate Research 12, no. 3 (2009): 183. http://dx.doi.org/10.22329/amr.v12i3.659.

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We present a data-based perspective concerning the Maclean’s magazine (November 17, 2003) rankings of Canadian universities, including two cluster analyses and other nonparametric analyses. These data are similar to those in recent university ranking exercises conducted by other magazines, such as U.S. News. In many cases, the cluster procedure showed that universities actually resemble and relate to each other in a manner different from their formal classification and final rank ordering by Maclean’s. Several pitfalls in ranking procedures, related to unreliable relationships among specific i

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Nassa, Anil Kumar, and Jagdish Arora. "Revisiting Ranking of Academic Institutions." DESIDOC Journal of Library & Information Technology 41, no. 1 (2021): 5–19. http://dx.doi.org/10.14429/djlit.41.1.16673.

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The article provides an overview of ranking systems including its historical evolution, use of rankings by different stakeholders, ranking indicators, merits and demerits of different ranking systems and performance of Indian universities in in past one decade in global ranking systems. The article briefly describes nine global ranking systems and compares them based on weightage assigned to different categories of indicators and source of data used for ranking of HEIs. Lastly, article provides statistical inter-correlation amongst various ranking systems as well as intra-correlation within ra

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Lang, Liqian, Yan Wang, Qinghua Chen, and Tao Zheng. "University ranking based on faculty hiring network with minimum weighted violation rankings." International Journal of Modern Physics C 30, no. 07 (2019): 1940017. http://dx.doi.org/10.1142/s0129183119400175.

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University ranking arouses widespread interest among the society and the scientific community. It can cause resources to be allocated to the entity which has a higher ranking to make tremendous uneven distribution of resources such as funds, faculty, students and so on. Every year various controversial university rankings are issued by different institutions or individuals. However, they have to deal with a huge amount of data and cumbersome computing in their research. Furthermore, during the process of calculation, some key indicators are unreliable, subjective, and difficult to obtain or co

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Gaisenok, V. A., O. A. Naumovich, and V. V. Samokhval. "Correlational Relationships between Various International Rankings for Higher Education Institutions." Higher Education in Russia 27, no. 12 (2019): 20–28. http://dx.doi.org/10.31992/0869-3617-2018-27-12-20-28.

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The task of the investigation was to study the interrelations of the results of university rankings presented by various ranking agencies. Data on the number of the participants in the World Rankings has been presented for the Webometrics Ranking of World Universities (WRWU), Academic Ranking of World Universities (ARWU), Quacquarelli Symonds (QS), Times Higher Education (THE), U-Multirank, Round University Ranking (RUR), the Moscow International Rating “Three University Missions”, SCImago Institutions Rankings (SIR).The correlations have been studied by the methods of mathematical statistics

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Sumanth Reddy, Shiva, Anil Kumar B, Jahnavi S, Manjunath D R, Girish N, and Nandini C. "Enhancing Academic Excellence through Autonomy: A Data-Driven Analysis of NIRF Rankings in South Indian Higher Educational Institutions." Journal of Hacking Techniques, Digital Crime Prevention and Computer Virology 1, no. 3 (2025): 36–49. https://doi.org/10.46610/johtdcpcv.2024.v01i03.004.

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Published as part of the research "Impact on NIRF Rankings", this study examines how colleges in Andhra Pradesh, Tamil Nadu, Kerala, and Telangana are performing regarding their autonomous status under the National Institutional Ranking Framework (NIRF). Using a detailed dataset from 2016–2023, the study employs advanced statistical techniques, including descriptive statistics, paired t-tests, Wilcoxonsigned-rank tests, and multiple regression models to uncover differences in rankings of NIRF before and after autonomy.Time series analysis with trend lines and moving averages can identify signi

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Udartseva, Olga. "Assessing Results of University Research Activities: Indicators of Openness in Rankings." Science Management: Theory and Practice 6, no. 4 (2024): 29–47. https://doi.org/10.19181/smtp.2024.6.4.2.

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Open access to scientific results and research data becomes an engine for the successful development of modern science. The article examines popular rankings of world universities: the CWTS Leiden Ranking, the Times Higher Education World University Rankings (THE Rankings), the QS World University Rankings (QS), the SCImago Institutions Rankings (SIR) and the Academic Ranking of World Universities (ARWU). The objective of the research was to study the ranking methodologies in the ratings under consideration and to determine the place of the openness criteria in them. A comparative analysis of

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Sun, Hai-Lian, Seong-no Lee, and Man-Keun Kim. "Evaluation of Taekwondo Players' Ranking Using the PageRank Algorithm." World Society of Taekwondo Culture 14, no. 3 (2023): 19–34. http://dx.doi.org/10.18789/jwstc.2023.40.19.

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The purpose of this study is to apply the Olympic Kyorugi Ranking system and PageRank algorithm to the results of four world-class taekwondo competitions held from 2020 to 2023, including G20, G14, G10, and G6, to calculate the rankings. Based on the results, a comparison between the two algorithms is conducted to provide basic information for taekwondo ranking calculation methods. For the purpose of this study, information was collected from the official website of the World Taekwondo Federation. Use MS-Excel, PyCharm, and IBM SPSS (ver. 27) programs to process data. The results of this study

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27

Page, Stewart. "Rankings of Canadian Universities, 1995: More Problems in Interpretation." Canadian Journal of Higher Education 26, no. 2 (1996): 47–57. http://dx.doi.org/10.47678/cjhe.v26i2.183237.

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A critical perspective is presented in regard to rankings of Canadian universities by Maclean's magazine, November 20, 1995. Present comments are based, in part, on a previous analysis (Page, 1995) of the Maclean's rankings from 1993 and 1994. Several pitfalls in the ranking procedures, and results of some analyses of the 1995 ranking data, are briefly outlined.

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Węgrzyn, Tomasz. "The TMAI Model – Performance Of Portfolios Constructed On The Base Of Correlated And Uncorrelated Financial Ratios." Folia Oeconomica Stetinensia 14, no. 2 (2014): 125–39. http://dx.doi.org/10.1515/foli-2015-0002.

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Abstract The author studies portfolio performance. Companies are chosen to portfolios due to their position in the ranking that is constructed on the base of the chosen financial ratios. There are three rankings constructed on different number of financial ratios. Each ranking is constructed on the base of synthetic measure of development. The TMAI ranking is constructed on the base of 48 financial ratios, the TMAI_gr1 ranking is constructed on the base of 14 financial ratios that can be correlated and the TMAI_gr2 ranking is constructed on the base of 8 uncorrelated financial ratios. The auth

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Amir-Ahmadi, Pooyan, and Thorsten Drautzburg. "Identification and inference with ranking restrictions." Quantitative Economics 12, no. 1 (2021): 1–39. http://dx.doi.org/10.3982/qe1277.

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We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. Th

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Amir-Ahmadi, Pooyan, and Thorsten Drautzburg. "Identification and inference with ranking restrictions." Quantitative Economics 12, no. 1 (2021): 1–39. http://dx.doi.org/10.3982/qe1277.

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We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. Th

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Hadad, Yossi, Lea Friedman, and Aviad A. Israeli. "EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS." Journal of Business Economics and Management 6, no. 3 (2005): 145–53. http://dx.doi.org/10.3846/16111699.2005.9636103.

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This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

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Hadad, Yossi, Lea Friedman, and Aviad A. Israeli. "EVALUATING HOTEL ADVERTISEMENTS EFFICIENCY USING DATA ENVELOPMENT ANALYSIS." Journal of Business Economics and Management 5, no. 3 (2004): 133–41. http://dx.doi.org/10.3846/16111699.2004.9636077.

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This paper introduces popular methods for ranking alternatives with multiple inputs and multiple outputs in the DEA context. The ranking methods are based on different criteria. Consequently, the ranking of the alternatives are not always the same, particularly as regards the best alternative. The decision maker, however, must make an absolute decision as to the most favored alternative. This study proposes a new ranking method, which is based on the average of the highly correlated ranking method. The new method is applied on a case study of ranking hotels in Israel.

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Batubara, Abdul Karim, Franindya Purwaningtyas, and Raissa Amanda Putri. "University Webometrics Ranking Analysis using SWOT and Gap Analysis." Khizanah al-Hikmah : Jurnal Ilmu Perpustakaan, Informasi, dan Kearsipan 11, no. 2 (2023): 164–73. http://dx.doi.org/10.24252/kah.v11i2a2.

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The Webometrics ranking serves as a crucial metric for assessing the quality and online visibility of universities. In this investigation, the study employs a SWOT analysis approach to delineate the strengths, weaknesses, opportunities, and threats influencing university Webometrics rankings. The primary objective is to scrutinize Webometrics rankings through the dual lenses of SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) and Gap analysis. The approach includes collecting Webometrics ranking data from reliable sources, followed by an in-depth SWOT analysis to identify the fact

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Klimasara, Edward, та Andrzej P. Wierzbicki. "Examples of Objectiﬁed Multiple Criteria Ranking in the Selection of Infrastructural Projects". Journal of Telecommunications and Information Technology 8 (28 грудня 2018): 18–25. http://dx.doi.org/10.26636/jtit.2018.126718.

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The paper addresses the issue of multiple criteria rankings of infrastructural projects (buildings, roads, etc.). Although the amount of literature devoted to this subject is considerable, all methods proposed produce subjective rankings, dependent on a direct or indirect deﬁnition of weighting coeﬃcients applicable to subsequent evaluation criteria. Infrastructural projects are usually selected and approved collegially, however, by a group of decision makers with preferences that may potentially diﬀer signiﬁcantly. Therefore, an objectiﬁed ranking, independent from subjectively deﬁned weighti

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Kakarala, Ramakrishna. "Interpreting the Phase Spectrum in Fourier Analysis of Partial Ranking Data." Advances in Numerical Analysis 2012 (June 27, 2012): 1–15. http://dx.doi.org/10.1155/2012/579050.

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Whenever ranking data are collected, such as in elections, surveys, and database searches, it is frequently the case that partial rankings are available instead of, or sometimes in addition to, full rankings. Statistical methods for partial rankings have been discussed in the literature. However, there has been relatively little published on their Fourier analysis, perhaps because the abstract nature of the transforms involved impede insight. This paper provides as its novel contributions an analysis of the Fourier transform for partial rankings, with particular attention to the first three ra

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Cheng, Min-Yuan, Cheng-Wei Su, Ming-Hsiu Tsai, and Kuo-Shian Lin. "DATA PREPROCESSING FOR ARTIFICIAL NEURAL NETWORK APPLICATIONS IN PRIORITIZING RAILROAD PROJECTS – A PRACTICAL EXPERIENCE IN TAIWAN." Journal of Civil Engineering and Management 18, no. 4 (2012): 483–94. http://dx.doi.org/10.3846/13923730.2012.699914.

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Financial constraints necessitate the tradeoff among proposed railroad projects, so that the project priorities for implementation and budget allocation need to be determined by the ranking mechanisms in the government. At present, the Taiwan central government prioritizes funding allocations primarily using the analytic hierarchy process (AHP), a methodology that permits the synthesizing of subjective judgments systematically and logically into objective consensus. However, due to the coopetition and heterogeneity of railway projects, the proper priorities of railroad projects could not be al

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Martyniuk, T. B., L. V. Krupelnitskyi, and B. I. Krukivskyi. "REGULAR COMPUTATIONAL STRUCTURE FOR DATA RANKING." Information technology and computer engineering 52, no. 3 (2021): 70–76. http://dx.doi.org/10.31649/1999-9941-2021-52-3-70-76.

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Chang, Yi, Anlei Dong, Pranam Kolari, et al. "Improving recency ranking using twitter data." ACM Transactions on Intelligent Systems and Technology 4, no. 1 (2013): 1–24. http://dx.doi.org/10.1145/2414425.2414429.

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Yiu, Man Lung, Hua Lu, Nikos Mamoulis, and Michail Vaitis. "Ranking Spatial Data by Quality Preferences." IEEE Transactions on Knowledge and Data Engineering 23, no. 3 (2011): 433–46. http://dx.doi.org/10.1109/tkde.2010.119.

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Clements, Margaret. "Ranking Data Sets - an easy method." Teaching Statistics 13, no. 1 (1991): 11. http://dx.doi.org/10.1111/j.1467-9639.1991.tb00143.x.

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Böckenholt, Ulf. "Thurstonian representation for partial ranking data." British Journal of Mathematical and Statistical Psychology 45, no. 1 (1992): 31–49. http://dx.doi.org/10.1111/j.2044-8317.1992.tb00976.x.

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Eftekhar, Milad, and Nick Koudas. "Partitioning and ranking tagged data sources." Proceedings of the VLDB Endowment 6, no. 4 (2013): 229–40. http://dx.doi.org/10.14778/2535570.2488330.

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Hewett, Rattikorn, and Phongphun Kijsanayothin. "Tumor classification ranking from microarray data." BMC Genomics 9, Suppl 2 (2008): S21. http://dx.doi.org/10.1186/1471-2164-9-s2-s21.

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Palma, Marco A. "Improving the prediction of ranking data." Empirical Economics 53, no. 4 (2016): 1681–710. http://dx.doi.org/10.1007/s00181-016-1169-2.

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Xu, Hang, Mayer Alvo, and Philip L. H. Yu. "Angle-based models for ranking data." Computational Statistics & Data Analysis 121 (May 2018): 113–36. http://dx.doi.org/10.1016/j.csda.2017.12.004.

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Bonomi, Luca, and Xiaoqian Jiang. "Patient ranking with temporally annotated data." Journal of Biomedical Informatics 78 (February 2018): 43–53. http://dx.doi.org/10.1016/j.jbi.2017.12.007.

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DAVID, H. A. "Ranking from unbalanced paired-comparison data." Biometrika 74, no. 2 (1987): 432–36. http://dx.doi.org/10.1093/biomet/74.2.432.

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Huang, Wenjie. "Data-driven satisficing measure and ranking." Journal of the Operational Research Society 71, no. 3 (2019): 456–74. http://dx.doi.org/10.1080/01605682.2019.1599779.

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Kilburn, Ashley, Brandon Kilburn, Kevin Hammond, and Denise Williams. "The Pursuit Of Excellence: 3rd-Party Rankings And Positioning Of Online Programs Through Quality And Value." American Journal of Business Education (AJBE) 13, no. 1 (2020): 19–26. http://dx.doi.org/10.19030/ajbe.v13i1.10327.

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Third-party ranking agencies (e.g., US News and World Report, Center for Online Education, CollegeChoice.net) produce rankings that are a popular publicly accessible search option for potential students as they sort online degree options. While higher education administration is keen to the possibility that most or all ranking systems risk methodological flaws, potential students are still on board with rankings and seem significantly steered by their results (Barkhorn, 2014). Monitoring rankings has become a positioning consideration for academic institutions. The disparity amongst ranking fa

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Sepehr, Romina, Maryam Shekofteh, Azam Shahbodaghi, and Sara Jambarsang. "Examining the Correlation between Presence in the University Ranking Systems with Countries’ Income and R&D Expenditure: A Study on Medical Sciences Universities." DESIDOC Journal of Library & Information Technology 45, no. 3 (2025): 217–23. https://doi.org/10.14429/djlit.20443.

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Abstract:

This study aims to examine the presence of various countries in international university ranking systems, specifically the Shanghai Academic Ranking of World Universities (ARWU), QS, and the Times Higher Education (THE) World University Rankings, focusing on medicine and related sciences. It also explores the relationship between the presence in the university ranking systems and country’s income level and spending on Research and Development (R&D). Data was gathered from the websites of the three international university ranking systems, as well as from the Organisation for Economic Co-op

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