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Journal articles on the topic 'Given data'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Zadelaar, Jacqueline N., Joost A. Agelink van Rentergem, and Hilde M. Huizenga. "Univariate comparisons given aggregated normative data." Clinical Neuropsychologist 31, no. 6-7 (2017): 1155–72. http://dx.doi.org/10.1080/13854046.2017.1348542.

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2

Plischke, Elmar, Emanuele Borgonovo, and Curtis L. Smith. "Global sensitivity measures from given data." European Journal of Operational Research 226, no. 3 (2013): 536–50. http://dx.doi.org/10.1016/j.ejor.2012.11.047.

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3

McCormick, Tyler H. "The "Given Data" Paradigm Undermines Both Cultures." Observational Studies 7, no. 1 (2021): 157–59. http://dx.doi.org/10.1353/obs.2021.0002.

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4

Stensholt, Eivind. "Regular matrix polynomials with given spectral data." Linear Algebra and its Applications 121 (August 1989): 666–73. http://dx.doi.org/10.1016/s0024-3795(16)30317-2.

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5

Zhang, Li-Chun. "Generalised Regression Estimation Given Imperfectly Matched Auxiliary Data." Journal of Official Statistics 37, no. 1 (2021): 239–55. http://dx.doi.org/10.2478/jos-2021-0010.

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Abstract Generalised regression estimation allows one to make use of available auxiliary information in survey sampling. We develop three types of generalised regression estimator when the auxiliary data cannot be matched perfectly to the sample units, so that the standard estimator is inapplicable. The inference remains design-based. Consistency of the proposed estimators is either given by construction or else can be tested given the observed sample and links. Mean square errors can be estimated. A simulation study is used to explore the potentials of the proposed estimators.

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6

Hu, Zhengbing, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, and Viktoriia O. Samitova. "Fuzzy Clustering Data Given in the Ordinal Scale." International Journal of Intelligent Systems and Applications 9, no. 1 (2017): 67–74. http://dx.doi.org/10.5815/ijisa.2017.01.07.

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7

Fejzić, Hajrudin. "Numerical Integration of Functions Given by Data Points." Sarajevo Journal of Mathematics 4, no. 1 (2024): 31–38. http://dx.doi.org/10.5644/sjm.04.1.03.

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8

Mccloughan, Patrick, and Esmaiel Abounoori. "How to estimate market concentration given grouped data." Applied Economics 35, no. 8 (2003): 973–83. http://dx.doi.org/10.1080/0003648032000050603.

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9

Hushagen, Sam. "Sense Data and the Romantic Myth of the Given." Wordsworth Circle 53, no. 3 (2022): 311–33. http://dx.doi.org/10.1086/720738.

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10

PFANNKUCH, MAXINE, and AMANDA RUBICK. "AN EXPLORATION OF STUDENTS’ STATISTICAL THINKING WITH GIVEN DATA." STATISTICS EDUCATION RESEARCH JOURNAL 1, no. 2 (2002): 4–21. http://dx.doi.org/10.52041/serj.v1i2.562.

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This paper examines how two twelve-year-old students built up their recognition and understanding of relationships in a set of data. Using a small multivariate dataset created by Watson, Collis, Callingham and Moritz (1995), the students conducted an investigation of their choice in a pencil-and-paper environment. The students’ thinking across the three representations of cards, tables and graphs is analysed from the perspectives of transnumeration, consideration of variation, reasoning with statistical models, and integrating the statistical with the contextual, which were identified as funda

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11

YOSHIDA, Kotaro, Hidefumi WAKAMATSU, Eiji MORINAGA, Seichiro TSUTSUMI, and Takahiro KUBO. "Design of developable surfaces using the given data points." Journal of Advanced Mechanical Design, Systems, and Manufacturing 15, no. 5 (2021): JAMDSM0056. http://dx.doi.org/10.1299/jamdsm.2021jamdsm0056.

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12

O’Grady, Cathleen. "Given the same data, ecologists arrive at different conclusions." Science 387, no. 6738 (2025): 1026. https://doi.org/10.1126/science.adx2120.

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13

Bragina, D., and N. Molodchik. "BIG DATA IN HUMAN RESOURCE MANAGEMENT." Management of the Personnel and Intellectual Resources in Russia 9, no. 3 (2020): 76–80. http://dx.doi.org/10.12737/2305-7807-2020-76-80.

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The article discusses the possibilities of using big data in the field of human resources management, shows the difficulties that can be encountered when introducing these technologies into the work of the company. The main problems of the use of employee data by companies for the analysis, forecasting and improvement production indicators are given. Examples of companies that successfully use big data in their work are shown. Recommendations how to introduce the technology of big data analysis in the field of human resource management are given.

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14

Berk, Aaron, Yaniv Plan, and Ozgur Yilmaz. "On the Best Choice of Lasso Program Given Data Parameters." IEEE Transactions on Information Theory 68, no. 4 (2022): 2573–603. http://dx.doi.org/10.1109/tit.2021.3138772.

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15

Bloch, Evan M., Paul M. Ness, Aaron A. R. Tobian, and Jeremy Sugarman. "Revisiting Blood Safety Practices Given Emerging Data about Zika Virus." New England Journal of Medicine 378, no. 19 (2018): 1837–41. http://dx.doi.org/10.1056/nejmsb1704752.

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16

Buchstaber, V. M., and A. A. Kustarev. "Embedding theorems for quasi-toric manifolds given by combinatorial data." Izvestiya: Mathematics 79, no. 6 (2015): 1157–83. http://dx.doi.org/10.1070/im2015v079n06abeh002776.

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17

Bohnsack, E. "Continuous field approximation of experimentally given data by finite elements." Computers & Structures 63, no. 6 (1997): 1195–204. http://dx.doi.org/10.1016/s0045-7949(96)00395-1.

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18

Wang, Dongliang, Alan D. Hutson, and Jeffrey C. Miecznikowski. "L-moment estimation for parametric survival models given censored data." Statistical Methodology 7, no. 6 (2010): 655–67. http://dx.doi.org/10.1016/j.stamet.2010.07.002.

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19

Becker, William E., and John R. Powers. "Student performance, attrition, and class size given missing student data." Economics of Education Review 20, no. 4 (2001): 377–88. http://dx.doi.org/10.1016/s0272-7757(00)00060-1.

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20

Johnstone, D. J., and D. V. Lindley. "Bayesian inference given data ?significant at??: Tests of point hypotheses." Theory and Decision 38, no. 1 (1995): 51–60. http://dx.doi.org/10.1007/bf01083168.

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21

Standish, Paul. "Data Return: The Sense of the Given in Educational Research." Journal of the Philosophy of Education 35, no. 3 (2001): 497–518. http://dx.doi.org/10.1111/1467-9752.00240.

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22

Lai, Keke. "A Simple Analytic Confidence Interval for CFI Given Nonnormal Data." Structural Equation Modeling: A Multidisciplinary Journal 26, no. 5 (2019): 757–77. http://dx.doi.org/10.1080/10705511.2018.1562351.

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23

Tsar’kov, I. G. "Stability of unique solvability of quasilinear equations given additional data." Mathematical Notes 90, no. 5-6 (2011): 894–919. http://dx.doi.org/10.1134/s0001434611110289.

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24

Kaneko, Akira, and Rina Nagahama. "Structure of Total Reconstructed Sets from Given Two Projection Data." Electronic Notes in Discrete Mathematics 20 (July 2005): 27–46. http://dx.doi.org/10.1016/j.endm.2005.05.055.

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25

Wen, Zhiyuan, Lijuan Zhou, and Meirong Zhang. "Optimal Potentials of Measure Differential Equations with Given Spectral Data." Journal of Optimization Theory and Applications 184, no. 1 (2018): 139–61. http://dx.doi.org/10.1007/s10957-018-01462-y.

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26

Pardeshi, Kanad, Itai Shapira, Ariel Procaccia, and Aarti Singh. "Learning Decision-Making Functions Given Cardinal and Ordinal Consensus Data." Proceedings of the AAAI Symposium Series 3, no. 1 (2024): 572. http://dx.doi.org/10.1609/aaaiss.v3i1.31280.

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Decision-making and reaching consensus are an integral part of everyday life, and studying how individuals reach these decisions is an important problem in psychology, economics, and social choice theory. Our work develops methods and theory for learning the nature of decisions reached upon by individual decision makers or groups of individuals using data. We consider two tasks, where we have access to data on: 1) Cardinal utilities for d individuals with cardinal consensus values that the group or decision maker arrives at, 2) Cardinal utilities for d individuals for pairs of actions, with or

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27

Laneev, Evgenii, and Ekaterina Ponomarenko. "ON A LINEAR INVERSE POTENTIAL PROBLEM WITH APPROXIMATE DATA ON THE POTENTIAL FIELD ON AN APPROXIMATELY GIVEN SURFACE." Eurasian Mathematical Journal 14, no. 1 (2023): 55–70. http://dx.doi.org/10.32523/2077-9879-2023-14-1-55-70.

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28

BARNA, Andrii, and Roman KAMINSKY. "ANALYSIS OF THE EFFICIENCY OF DATA CHUNKING METHODS FOR DATA DEDUBLICATION SYSTEMS." Herald of Khmelnytskyi National University. Technical sciences 315, no. 6(1) (2022): 24–27. http://dx.doi.org/10.31891/2307-5732-2022-315-6-24-27.

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There is a significant increase in the amount of data that needs to be stored worldwide. More and more companies are turning their attention to deduplication systems, which effectively increase data warehouse volume and reduce storage costs. Deduplication not only reduces the overall amount of information in storage but also reduces the load on networks by eliminating the need to retransmit duplicate data. In this work, we considered the stages that any deduplication system includes, namely chunking, hashing and indexing, mapping. The effectiveness of deduplication systems primarily depends on

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29

McNerney, James, Zachary A. Needell, Michael T. Chang, Marco Miotti, and Jessika E. Trancik. "TripEnergy: Estimating Personal Vehicle Energy Consumption Given Limited Travel Survey Data." Transportation Research Record: Journal of the Transportation Research Board 2628, no. 1 (2017): 58–66. http://dx.doi.org/10.3141/2628-07.

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Estimating personal vehicle energy consumption is important for nationwide climate policy, local and statewide environmental policy, and technology planning. Transportation energy use is complex, depending on vehicle performance and the driving behavior of individuals, as well as on travel patterns of cities and regions. Previous studies combine large samples of travel behavior with fixed estimates of per mile fuel economy or use detailed models of vehicles with limited samples of travel behavior. This paper presents a model for estimating privately operated vehicle energy consumption—TripEner

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30

Kim, Jang-Sun, and Hoon-Gee Yang. "Coherent Detection in Sea Clutter Given Acquisition Data for Long Duration." Journal of Korean Institute of Electromagnetic Engineering and Science 31, no. 10 (2020): 877–85. http://dx.doi.org/10.5515/kjkiees.2020.31.10.877.

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31

Taoyang Wu, V. Moulton, and M. Steel. "Refining Phylogenetic Trees Given Additional Data: An Algorithm Based on Parsimony." IEEE/ACM Transactions on Computational Biology and Bioinformatics 6, no. 1 (2009): 118–25. http://dx.doi.org/10.1109/tcbb.2008.100.

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32

Sadeh, Ilan. "Bounds on Data Compression Ratio with a Given Tolerable Error Probability." Probability in the Engineering and Informational Sciences 12, no. 2 (1998): 189–210. http://dx.doi.org/10.1017/s0269964800005143.

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The paper treats data compression from the viewpoint of probability theory where a certain error probability is tolerable. We obtain bounds for the minimal rate given an error probability for blockcoding of general stationary ergodic sources. An application of the theory of large deviations provides numerical methods to compute for memoryless sources, the minimal compression rate given a tolerable error probability. Interesting connections between Cramer's functions and Shannon's theory for lossy coding are found.

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33

Heit, Evan. "What is the probability of the Bayesian model, given the data?" Behavioral and Brain Sciences 24, no. 4 (2001): 672–73. http://dx.doi.org/10.1017/s0140525x01390088.

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The great advantage of Tenenbaum and Griffiths's model is that it incorporates both specific and general prior knowledge into category learning. Two phenomena are presented as supporting the detailed assumptions of this model. However, one phenomenon, effects of diversity, does not seem to require these assumptions, and the other phenomenon, effects of sample size, is not representative of most reported results. [Tenenbaum & Griffiths]

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34

Xu, Shu-Fang. "On the Jacobi Matrix Inverse Eigenvalue Problem with Mixed Given Data." SIAM Journal on Matrix Analysis and Applications 17, no. 3 (1996): 632–39. http://dx.doi.org/10.1137/s089547989122065x.

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35

Crupi, Marilena, and Rosanna Utano. "Classes of Graded Ideals with Given Data in the Exterior Algebra." Communications in Algebra 35, no. 8 (2007): 2386–408. http://dx.doi.org/10.1080/00927870701325959.

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36

Lai, Keke. "Better Confidence Intervals for RMSEA in Growth Models given Nonnormal Data." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 2 (2019): 255–74. http://dx.doi.org/10.1080/10705511.2019.1643246.

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37

Lai, Keke. "Correct Point Estimator and Confidence Interval for RMSEA Given Categorical Data." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 5 (2019): 678–95. http://dx.doi.org/10.1080/10705511.2019.1687302.

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38

Havrylenko, Ye, Yu Kholodnyak, and M. Miroshnychenko. "ALGORITHM FOR MODELING ONE-DIMENSIONAL CONTOURS ACCORDING TO THE GIVEN DATA." Scientific bulletin of the Tavria State Agrotechnological University 1, no. 12 (2022): 1–9. http://dx.doi.org/10.31388/2220-8674-2022-1-22.

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39

Hagoort, Peter, and Jos van Berkum. "Beyond the sentence given." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1481 (2007): 801–11. http://dx.doi.org/10.1098/rstb.2007.2089.

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A central and influential idea among researchers of language is that our language faculty is organized according to Fregean compositionality, which states that the meaning of an utterance is a function of the meaning of its parts and of the syntactic rules by which these parts are combined. Since the domain of syntactic rules is the sentence, the implication of this idea is that language interpretation takes place in a two-step fashion. First, the meaning of a sentence is computed. In a second step, the sentence meaning is integrated with information from prior discourse, world knowledge, info

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40

Rivera, Joseph. "The Myth of the Given?" Philosophy Today 62, no. 1 (2018): 181–97. http://dx.doi.org/10.5840/philtoday201837207.

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The theological turn in phenomenology continues to generate cross-disciplinary discussion among philosophers and theologians concerning the scope and boundaries of what counts as a “phenomenon.” This essay suggests that the very idea of the given, a term so important for Husserl, Heidegger, Henry and Marion, can be reassessed from the point of view of Wilifred Sellars’s discussion of the myth of the “immediate” given. Sometimes phenomenology is understood to involve the skill of unveiling immediate data that appear as “phenomena” to a conscious and wakeful ego. In conversation with Jean-Luc Ma

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41

Satyanarayana, Y., D. Chanchal, and G. R. K. Murty. "PROFILE: A Microsoft Quick Basic program for retrieving data along a given profile from gridded data." Computers & Geosciences 23, no. 1 (1997): 127–31. http://dx.doi.org/10.1016/s0098-3004(96)00077-5.

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42

Taylor, Paul. "Commentary: Tempering expectations of screening: what is the most authoritative advice we can give, given the data that we have?" International Journal of Epidemiology 44, no. 1 (2015): 280–82. http://dx.doi.org/10.1093/ije/dyu269.

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43

Gniazdowski, Zenon, and Michał Grabowski. "Numerical Coding of Nominal Data." Zeszyty Naukowe WWSI 9, no. 12 (2015): 53——61. https://doi.org/10.26348/znwwsi.12.53.

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In this paper, a novel approach for coding nominal data is proposed. For the given nominal data, a rank in a form of complex number is assigned. The proposed method does not lose any information about the attribute and brings other properties previously unknown. The approach based on these knew properties can been used for classification. The analyzed example shows that classification with the use of coded nominal data or both numerical as well as coded nominal data is more effective than the classification, which uses only numerical data.

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44

Karwański, Marek, Michał Gostkowski, and Piotr Jałowiecki. "Loss given default modeling: an application to data from a Polish bank." Journal of Risk Model Validation 9, no. 3 (2015): 23–40. http://dx.doi.org/10.21314/jrmv.2015.139.

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45

Al-Hossain, Abdullah Y. "Predictive Inference from the Exponentiated Weibull Model Given Adaptive Progressive Censored Data." Applied Mathematics & Information Sciences 10, no. 3 (2016): 1177–84. http://dx.doi.org/10.18576/amis/100336.

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46

Altomare, Angela, Maria Cristina Burla, Corrado Cuocci, et al. "MAD techniques applied to powder data: finding the structure given the substructure." Acta Crystallographica Section A Foundations of Crystallography 65, no. 4 (2009): 291–99. http://dx.doi.org/10.1107/s0108767309016304.

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47

Demirtas, Hakan. "A method for multivariate ordinal data generation given marginal distributions and correlations." Journal of Statistical Computation and Simulation 76, no. 11 (2006): 1017–25. http://dx.doi.org/10.1080/10629360600569246.

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48

Roehr, B. "FDA given new powers over data reporting to national clinical trials registry." BMJ 345, oct02 2 (2012): e6629-e6629. http://dx.doi.org/10.1136/bmj.e6629.

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49

Paninski, Liam. "A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data." IEEE Transactions on Information Theory 54, no. 10 (2008): 4750–55. http://dx.doi.org/10.1109/tit.2008.928987.

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50

Onyewuchi, Urenna P., Abdollah Shafieezadeh, Miroslav M. Begovic, and Reginald DesRoches. "A Probabilistic Framework for Prioritizing Wood Pole Inspections Given Pole Geospatial Data." IEEE Transactions on Smart Grid 6, no. 2 (2015): 973–79. http://dx.doi.org/10.1109/tsg.2015.2391183.

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