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Artykuły w czasopismach na temat „Empirical methods”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Lindley, D. V., J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Mathematical Gazette 74, no. 467 (1990): 91. http://dx.doi.org/10.2307/3618894.

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2

Bagghi, Parthasarathy, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Journal of the American Statistical Association 86, no. 413 (1991): 244. http://dx.doi.org/10.2307/2289739.

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3

Angus, John E. "Empirical Bayes Methods." Technometrics 33, no. 2 (1991): 243–45. http://dx.doi.org/10.1080/00401706.1991.10484821.

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4

Stephenson, W. Robert. "Empirical Bayes Methods." Journal of Quality Technology 22, no. 3 (1990): 249–50. http://dx.doi.org/10.1080/00224065.1990.11979250.

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5

Young, Karen, J. Maritz, and T. Lwin. "Empirical Bayes Methods." Applied Statistics 41, no. 3 (1992): 604. http://dx.doi.org/10.2307/2348097.

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6

Denham, Mike, J. S. Maritz, and T. Lwin. "Empirical Bayes Methods." Statistician 39, no. 1 (1990): 97. http://dx.doi.org/10.2307/2348214.

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7

Schneider-Mayerson, Matthew, Alexa Weik von Mossner, and W. P. Małecki. "Empirical Ecocriticism: Environmental Texts and Empirical Methods." ISLE: Interdisciplinary Studies in Literature and Environment 27, no. 2 (2020): 327–36. http://dx.doi.org/10.1093/isle/isaa022.

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8

ter Beek, Maurice H., and Alessio Ferrari. "Empirical Formal Methods: Guidelines for Performing Empirical Studies on Formal Methods." Software 1, no. 4 (2022): 381–416. http://dx.doi.org/10.3390/software1040017.

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Empirical studies on formal methods and tools are rare. In this paper, we provide guidelines for such studies. We mention their main ingredients and then define nine different study strategies (usability testing, laboratory experiments with software and human subjects, case studies, qualitative studies, surveys, judgement studies, systematic literature reviews, and systematic mapping studies) and discuss for each of them their crucial characteristics, the difficulties of applying them to formal methods and tools, typical threats to validity, their maturity in formal methods, pointers to extern

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9

Wolstein, Benjamin. "Five Empirical Psychoanalytic Methods." Contemporary Psychoanalysis 26, no. 2 (1990): 237–56. http://dx.doi.org/10.1080/00107530.1990.10746657.

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10

Laird, Nan M., and Thomas A. Louis. "Empirical Bayes Ranking Methods." Journal of Educational Statistics 14, no. 1 (1989): 29–46. http://dx.doi.org/10.3102/10769986014001029.

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Ranking problems arise in setting priorities for investigations, in providing a simple summary of performance, in comparing objects in a manner robust to measurement scale, and in a wide variety of other applications. Commonly, rankings are computed from measurements that depend on the true attribute. Using the Gaussian model, we propose and compare methods for using these measurements to estimate the ranks of the underlying attributes and show that those based on an empirical Bayes model produce estimates that differ from ranking observed data. These differences result both from the effect of

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11

Laird, Nan M., and Thomas A. Louis. "Empirical Bayes Ranking Methods." Journal of Educational Statistics 14, no. 1 (1989): 29. http://dx.doi.org/10.2307/1164724.

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12

Casella, George. "Illustrating empirical Bayes methods." Chemometrics and Intelligent Laboratory Systems 16, no. 2 (1992): 107–25. http://dx.doi.org/10.1016/0169-7439(92)80050-e.

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13

Gu, Jiaying, and Roger Koenker. "Empirical Bayesball Remixed: Empirical Bayes Methods for Longitudinal Data." Journal of Applied Econometrics 32, no. 3 (2016): 575–99. http://dx.doi.org/10.1002/jae.2530.

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14

McKeown, Timothy. "The empirical study of great power politics." Qualitative & Multi-Method Research 15, no. 2 (2017): 36–39. https://doi.org/10.5281/zenodo.2563184.

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If international politics is about “power,” then what do governments do once they have it? In Economic Interdependence and War, Dale Copeland suggests that they often use it to gain control of resources on favorable terms, then use that control to leverage additional gains in control and wealth.

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15

Engel, Christoph. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 5. http://dx.doi.org/10.1628/093245617x15096094637968.

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16

Gilbert, Daniel, and Martin T. Wells. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 24. http://dx.doi.org/10.1628/093245617x15096094637977.

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17

Engel, Christoph, and Urs Schweizer. "Empirical Methods for the Law." Journal of Institutional and Theoretical Economics 174, no. 1 (2018): 1. http://dx.doi.org/10.1628/093245618x15138375893649.

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18

Maritz, J. S., and T. Lwin. "Empirical Bayes Methods, 2nd Edition." Biometrics 46, no. 3 (1990): 886. http://dx.doi.org/10.2307/2532124.

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19

Murrell, John N. "Semi-empirical electronic structure methods." Journal of Molecular Structure: THEOCHEM 424, no. 1-2 (1998): 93–99. http://dx.doi.org/10.1016/s0166-1280(97)00234-0.

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20

Büscher, Monika, and John Urry. "Mobile Methods and the Empirical." European Journal of Social Theory 12, no. 1 (2009): 99–116. http://dx.doi.org/10.1177/1368431008099642.

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21

Backhaus, Klaus, Robert Wilken, Markus Voeth, and Christina Sichtmann. "An Empirical Comparison of Methods." International Journal of Market Research 47, no. 5 (2005): 541–60. http://dx.doi.org/10.1177/147078530504700502.

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22

Eisenberg, Theodore. "Empirical Methods and the Law." Journal of the American Statistical Association 95, no. 450 (2000): 665–69. http://dx.doi.org/10.1080/01621459.2000.10474247.

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23

Kingdon, Geeta Gandhi. "Empirical Research Methods in Education." Contemporary Education Dialogue 3, no. 2 (2006): 171–74. http://dx.doi.org/10.1177/0973184913411116.

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24

Bowen, Harry P. "Empirical methods for international trade." Journal of International Economics 27, no. 1-2 (1989): 191–96. http://dx.doi.org/10.1016/0022-1996(89)90087-1.

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25

Persson, Kirsten, and David Shaw. "Empirical Methods in Animal Ethics." Journal of Agricultural and Environmental Ethics 28, no. 5 (2015): 853–66. http://dx.doi.org/10.1007/s10806-015-9560-0.

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26

P., S. Aithal, and Aithal Shubhrajyotsna. "Use of AI-Based GPTs in Experimental, Empirical, and Exploratory Research Methods." International Journal of Case Studies in Business, IT, and Education (IJCSBE) 7, no. 4 (2023): 33–47. https://doi.org/10.5281/zenodo.8407794.

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<strong>Purpose:</strong> <em>Academic research is a systematic study based on the creation of new knowledge or new interpretations of existing knowledge. As per one school of studies, academic research uses three generic methods known as experimental research, empirical research, and exploratory research. The use of recently developed AI-based GPTs have shown their ability to provide information from global sources without directly copying from any one source. Thus, it is an interesting research problem on how AI-based GPTs can be used in all three types of research methods. </em> <strong>Met

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27

Regenwetter, Michel, Aeri Kim, Arthur Kantor, and Moon-Ho R. Ho. "The Unexpected Empirical Consensus Among Consensus Methods." Psychological Science 18, no. 7 (2007): 629–35. http://dx.doi.org/10.1111/j.1467-9280.2007.01950.x.

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In economics and political science, the theoretical literature on social choice routinely highlights worst-case scenarios and emphasizes the nonexistence of a universally best voting method. Behavioral social choice is grounded in psychology and tackles consensus methods descriptively and empirically. We analyzed four elections of the American Psychological Association using a state-of-the-art multimodel, multimethod approach. These elections provide rare access to (likely sincere) preferences of large numbers of decision makers over five choice alternatives. We determined the outcomes accordi

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28

Foster, Michael E., and Karl Sohlberg. "Empirically corrected DFT and semi-empirical methods for non-bonding interactions." Phys. Chem. Chem. Phys. 12, no. 2 (2010): 307–22. http://dx.doi.org/10.1039/b912859j.

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29

Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.

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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly

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30

Sampaio, Américo, Alexandre Vasconcelos, and Pedro R. Falcone Sampaio. "Assessing agile methods: an empirical study." Journal of the Brazilian Computer Society 10, no. 2 (2004): 22–41. http://dx.doi.org/10.1590/s0104-65002004000300003.

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31

Carpenter, Craig Wesley, Anders Van Sandt, and Scott Loveridge. "Empirical methods in business location research." Regional Studies, Regional Science 8, no. 1 (2021): 344–61. http://dx.doi.org/10.1080/21681376.2021.1976261.

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32

Sobrinho, Oswaldo Palma Lopes, Wady Lima Castro Júnior, Leonardo Nazário Silva dos Santos, Gerlange Soares da Silva, Álvaro Itaúna Schalcher Pereira, and Germanna Gouveia Tavares. "Empirical methods for reference evapotranspiration estimation." Scientia Agraria Paranaensis 19, no. 3 (2020): 203–10. http://dx.doi.org/10.18188/sap.v19i3.21487.

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The reference evapotranspiration (ETo) is a parameter applied in meteorological, climatological and hydrological studies being of great importance for the planning, management and management of water resources. Due to the need for knowledge about the loss of water from vegetated surfaces, researchers have developed a variety of methods for estimating ETo, some of which are well accepted, while others are criticized, such as the Penman-Monteith-FAO (PM-FAO) meteorological data, which is not always available by limiting its use. The PM-FAO method has been used for decades to be considered intern

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33

Leyland, Alastair H., and Carolyn A. Davies. "Empirical Bayes methods for disease mapping." Statistical Methods in Medical Research 14, no. 1 (2005): 17–34. http://dx.doi.org/10.1191/0962280205sm387oa.

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34

Tate, Robyn L., Christine Taylor, and Vanessa Aird. "Applying Empirical Methods in Clinical Practice." Journal of Head Trauma Rehabilitation 28, no. 2 (2013): 77–88. http://dx.doi.org/10.1097/htr.0b013e31824e103e.

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35

Tufis, D. "Empirical Methods for Exploiting Parallel Texts." Literary and Linguistic Computing 17, no. 3 (2002): 368–70. http://dx.doi.org/10.1093/llc/17.3.368.

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36

Martinsek, Adam T. "Empirical bayes methods in sequential estimation." Sequential Analysis 6, no. 2 (1987): 119–37. http://dx.doi.org/10.1080/07474948708836120.

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37

Reed, David, Doug Baldwin, Michael Clancy, Allen Downey, and Stuart Hansen. "Integrating empirical methods into computer science." ACM SIGCSE Bulletin 34, no. 1 (2002): 48–49. http://dx.doi.org/10.1145/563517.563356.

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38

Sampson, Geoffrey. "Quantifying the shift towards empirical methods." International Journal of Corpus Linguistics 10, no. 1 (2005): 15–36. http://dx.doi.org/10.1075/ijcl.10.1.02sam.

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In recent decades there has been a trend towards greater use of empirical data, for instance corpus data, within linguistics. I analyse a sample of linguistics articles from the past half-century in order to establish a detailed profile for this trend. Based on consistent criteria for classifying papers as evidence-based, intuition-based, or neutral, the resulting profile shows that the trend (i) is real, but (ii) is strikingly weaker in general linguistics than in the special subfield of computational linguistics, and (iii) appears to have begun to go into reverse.

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39

Nelson, P. G. "Semi-empirical Methods of Quantum Chemistry." Analytica Chimica Acta 186 (1986): 340–41. http://dx.doi.org/10.1016/s0003-2670(00)81812-7.

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40

Haghani, Milad, and Majid Sarvi. "Crowd behaviour and motion: Empirical methods." Transportation Research Part B: Methodological 107 (January 2018): 253–94. http://dx.doi.org/10.1016/j.trb.2017.06.017.

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41

Flynn, Barbara B., Sadao Sakakibara, Roger G. Schroeder, Kimberly A. Bates, and E. James Flynn. "Empirical research methods in operations management." Journal of Operations Management 9, no. 2 (1990): 250–84. http://dx.doi.org/10.1016/0272-6963(90)90098-x.

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42

Greis, Noel P., and C. Zachary Gilstein. "Empirical Bayes methods for telecommunications forecasting." International Journal of Forecasting 7, no. 2 (1991): 183–97. http://dx.doi.org/10.1016/0169-2070(91)90053-x.

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Sampaio, Américo, Alexandre Vasconcelos, and Pedro R. Falcone Sampaio. "Assessing agile methods: An empirical study." Journal of the Brazilian Computer Society 10, no. 2 (2004): 21–48. http://dx.doi.org/10.1007/bf03192357.

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44

Lauer, Gerhard. "Introduction: Empirical Methods in Literary Studies." Journal of Literary Theory 9, no. 1 (2015): 1–3. http://dx.doi.org/10.1515/jlt-2015-0001.

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45

Sommerfeld, Thomas, and Rebecca J. Weber. "Empirical Correlation Methods for Temporary Anions." Journal of Physical Chemistry A 115, no. 24 (2011): 6675–82. http://dx.doi.org/10.1021/jp202817d.

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46

Bell, Stephen. "Semi-empirical methods of quantum chemistry." Endeavour 10, no. 1 (1986): 52–53. http://dx.doi.org/10.1016/0160-9327(86)90065-7.

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47

Stenbakken, G. N., and H. Liu. "Empirical Modeling Methods Using Partial Data." IEEE Transactions on Instrumentation and Measurement 53, no. 2 (2004): 271–76. http://dx.doi.org/10.1109/tim.2003.822481.

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48

Efron, Bradley. "Empirical Bayes Methods for Combining Likelihoods." Journal of the American Statistical Association 91, no. 434 (1996): 538–50. http://dx.doi.org/10.1080/01621459.1996.10476919.

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Sridhar, M. R., and M. M. Yovanovich. "Empirical methods to predict Vickers microhardness." Wear 193, no. 1 (1996): 91–98. http://dx.doi.org/10.1016/0043-1648(95)06681-0.

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Sauder, T. "Fidelity of Cyber-Physical Empirical Methods." Experimental Techniques 44, no. 6 (2020): 669–85. http://dx.doi.org/10.1007/s40799-020-00372-x.

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AbstractCyber-physical empirical methods enable to address problems that classical empirical methods alone, or models alone, cannot address in a satisfactory way. In CPEMs, the substructures are interconnected through a control system that includes sensors and actuators, having their own dynamics. The present paper addresses how the fidelity of CPEMs, that is the degree to which they reproduce the behaviour of the real system under study, is affected by the presence of this control system. We describe an analysis method that enables the designer of a CPEM to (1) identify the artefacts (such as

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