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Journal articles on the topic 'Given data'

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Author: Grafiati

Published: 9 March 2023

Last updated: 10 March 2023

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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 (July 5, 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 (May 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 (March 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 (January 8, 2017): 67–74. http://dx.doi.org/10.5815/ijisa.2017.01.07.

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7

Mccloughan, Patrick, and Esmaiel Abounoori. "How to estimate market concentration given grouped data." Applied Economics 35, no. 8 (May 20, 2003): 973–83. http://dx.doi.org/10.1080/0003648032000050603.

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8

Hushagen, Sam. "Sense Data and the Romantic Myth of the Given." Wordsworth Circle 53, no. 3 (June 1, 2022): 311–33. http://dx.doi.org/10.1086/720738.

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9

PFANNKUCH, MAXINE, and AMANDA RUBICK. "AN EXPLORATION OF STUDENTS’ STATISTICAL THINKING WITH GIVEN DATA." STATISTICS EDUCATION RESEARCH JOURNAL 1, no. 2 (December 29, 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 fundamental statistical thinking elements in empirical enquiry in the framework of Wild and Pfannkuch (1999). The ways of thinking within each element across the representations are identified. In the analysis, references are also made to the types of statistical thinking present in the other ten students in the study. From the analysis we identified five issues that should be considered for determining how students construct meanings from data. They are: prior contextual and statistical knowledge; thinking at a higher level than constructed representations; actively representing and construing; the intertwinement of local and global thinking; and the changing statistical thinking dialogue across the representations. First published December 2002 at Statistics Education Research Journal: Archives

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10

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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11

Bragina, D., and N. Molodchik. "BIG DATA IN HUMAN RESOURCE MANAGEMENT." Management of the Personnel and Intellectual Resources in Russia 9, no. 3 (July 23, 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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12

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 (April 2022): 2573–603. http://dx.doi.org/10.1109/tit.2021.3138772.

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13

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 (May 10, 2018): 1837–41. http://dx.doi.org/10.1056/nejmsb1704752.

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14

Buchstaber, V. M., and A. A. Kustarev. "Embedding theorems for quasi-toric manifolds given by combinatorial data." Izvestiya: Mathematics 79, no. 6 (December 31, 2015): 1157–83. http://dx.doi.org/10.1070/im2015v079n06abeh002776.

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15

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

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16

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

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17

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

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18

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

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19

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

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20

Lai, Keke. "A Simple Analytic Confidence Interval for CFI Given Nonnormal Data." Structural Equation Modeling: A Multidisciplinary Journal 26, no. 5 (April 4, 2019): 757–77. http://dx.doi.org/10.1080/10705511.2018.1562351.

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21

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

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22

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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23

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 (December 18, 2018): 139–61. http://dx.doi.org/10.1007/s10957-018-01462-y.

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24

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 (January 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—TripEnergy—that accurately reconstructs detailed driving behavior across the United States and simulates vehicle performance for different driving conditions. The accuracy of this reconstruction was tested by using out-of-sample predictions, and the vehicle model was tested against microsimulation. TripEnergy consists of a demand model, linking GPS drive cycles to travel survey trips, and a vehicle model, efficiently simulating energy consumption across different types of driving. Because of its ability to link small-scale variation in vehicle technology and driver behavior with large-scale variation in travel patterns, it is expected to be useful for a variety of applications, including technology assessment, cost and energy savings from ecodriving, and the integration of electric vehicle technologies into the grid.

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25

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 (October 2020): 877–85. http://dx.doi.org/10.5515/kjkiees.2020.31.10.877.

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26

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 (January 2009): 118–25. http://dx.doi.org/10.1109/tcbb.2008.100.

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27

Sadeh, Ilan. "Bounds on Data Compression Ratio with a Given Tolerable Error Probability." Probability in the Engineering and Informational Sciences 12, no. 2 (April 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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28

Heit, Evan. "What is the probability of the Bayesian model, given the data?" Behavioral and Brain Sciences 24, no. 4 (August 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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29

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

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30

Crupi, Marilena, and Rosanna Utano. "Classes of Graded Ideals with Given Data in the Exterior Algebra." Communications in Algebra 35, no. 8 (July 19, 2007): 2386–408. http://dx.doi.org/10.1080/00927870701325959.

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31

Lai, Keke. "Better Confidence Intervals for RMSEA in Growth Models given Nonnormal Data." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 2 (September 24, 2019): 255–74. http://dx.doi.org/10.1080/10705511.2019.1643246.

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32

Lai, Keke. "Correct Point Estimator and Confidence Interval for RMSEA Given Categorical Data." Structural Equation Modeling: A Multidisciplinary Journal 27, no. 5 (December 18, 2019): 678–95. http://dx.doi.org/10.1080/10705511.2019.1687302.

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33

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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34

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 (February 1997): 127–31. http://dx.doi.org/10.1016/s0098-3004(96)00077-5.

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35

Hagoort, Peter, and Jos van Berkum. "Beyond the sentence given." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1481 (April 3, 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, information about the speaker and semantic information from extra-linguistic domains such as co-speech gestures or the visual world. Here, we present results from recordings of event-related brain potentials that are inconsistent with this classical two-step model of language interpretation. Our data support a one-step model in which knowledge about the context and the world, concomitant information from other modalities, and the speaker are brought to bear immediately, by the same fast-acting brain system that combines the meanings of individual words into a message-level representation. Underlying the one-step model is the immediacy assumption, according to which all available information will immediately be used to co-determine the interpretation of the speaker's message. Functional magnetic resonance imaging data that we collected indicate that Broca's area plays an important role in semantic unification. Language comprehension involves the rapid incorporation of information in a ‘single unification space’, coming from a broader range of cognitive domains than presupposed in the standard two-step model of interpretation.

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36

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 Marion’s volume Givenness and Revelation, I challenge the assumption that phenomena are immediate in their givenness. The final remarks concern the “how” of the givenness of theological data, and in particular, the phenomenon of the Trinity.

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37

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 (January 14, 2015): 280–82. http://dx.doi.org/10.1093/ije/dyu269.

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38

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 (September 2015): 23–40. http://dx.doi.org/10.21314/jrmv.2015.139.

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39

Al-Hossain, Abdullah Y. "Predictive Inference from the Exponentiated Weibull Model Given Adaptive Progressive Censored Data." Applied Mathematics & Information Sciences 10, no. 3 (May 1, 2016): 1177–84. http://dx.doi.org/10.18576/amis/100336.

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40

Altomare, Angela, Maria Cristina Burla, Corrado Cuocci, Carmelo Giacovazzo, Fabia Gozzo, Anna Moliterni, Giampiero Polidori, and Rosanna Rizzi. "MAD techniques applied to powder data: finding the structure given the substructure." Acta Crystallographica Section A Foundations of Crystallography 65, no. 4 (June 11, 2009): 291–99. http://dx.doi.org/10.1107/s0108767309016304.

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41

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

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42

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

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43

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

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44

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 (March 2015): 973–79. http://dx.doi.org/10.1109/tsg.2015.2391183.

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45

Monfared, Keivan Hassani, and Bryan L. Shader. "Construction of matrices with a given graph and prescribed interlaced spectral data." Linear Algebra and its Applications 438, no. 11 (June 2013): 4348–58. http://dx.doi.org/10.1016/j.laa.2013.01.036.

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46

Mukai, S., I. Sano, and Y. Okada. "Atmospheric correction for ocean color data given by ADEOS/OCTS and POLDER." Advances in Space Research 25, no. 5 (January 2000): 1025–28. http://dx.doi.org/10.1016/s0273-1177(99)00466-4.

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47

Reuter, Christina, Felix Brambring, and Thomas Hempel. "Increasing the Traceability Through Targeted Data Acquisition for Given Product Process Combinations." Procedia CIRP 52 (2016): 210–15. http://dx.doi.org/10.1016/j.procir.2016.07.039.

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48

Kiendl, F., and G. Güntherodt. "Computational methods to produce enhanced images out of given SNOM raw data." Ultramicroscopy 102, no. 4 (March 2005): 299–310. http://dx.doi.org/10.1016/j.ultramic.2004.10.010.

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49

Eun, Hee-Jue, Suk-Jin Kim, and Yong-Sung Kim. "An Algorithm for Finding a Reasonable Smooth Curve for Any Given Data." International Journal of Multimedia and Ubiquitous Engineering 11, no. 1 (January 31, 2016): 229–40. http://dx.doi.org/10.14257/ijmue.2016.11.1.23.

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50

Blackburn, David F. "Issues in reporting epidemiological studies: No data are given to support generalisation." BMJ 330, no. 7483 (January 13, 2005): 146.3. http://dx.doi.org/10.1136/bmj.330.7483.146-b.

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