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Journal articles on the topic 'Computer intensive methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Yakowitz, S. "Computer intensive statistical methods." Eos, Transactions American Geophysical Union 67, no. 20 (1986): 476. http://dx.doi.org/10.1029/eo067i020p00476-04.

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2

Ghosh, Subir. "Computer Intensive Methods in Statistics." Technometrics 62, no. 3 (2020): 415. http://dx.doi.org/10.1080/00401706.2020.1783946.

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3

Treble, John, and Eric W. Noreen. "Computer Intensive Methods for Hypothesis Testing:." Economic Journal 100, no. 401 (1990): 657. http://dx.doi.org/10.2307/2234172.

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4

Buckland, S. T., and E. W. Noreen. "Computer-Intensive Methods for Testing Hypotheses." Biometrics 46, no. 2 (1990): 540. http://dx.doi.org/10.2307/2531461.

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5

Ogle, Kiona. "A Collection of Computer-Intensive Methods." BioScience 57, no. 10 (2007): 886–87. http://dx.doi.org/10.1641/b571013.

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6

Hollas, E. A., and Eric W. Noreen. "Computer Intensive Methods for Testing Hypotheses." Statistician 39, no. 1 (1990): 89. http://dx.doi.org/10.2307/2348203.

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7

Politis, D. N. "Computer-intensive methods in statistical analysis." IEEE Signal Processing Magazine 15, no. 1 (1998): 39–55. http://dx.doi.org/10.1109/79.647042.

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8

Efron, Bradley. "Computer-Intensive Methods in Statistical Regression." SIAM Review 30, no. 3 (1988): 421–49. http://dx.doi.org/10.1137/1030093.

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9

Stanislaw, Harold. "Computer-Intensive Methods in Traffic Safety Research." International Journal of Occupational Safety and Ergonomics 8, no. 3 (2002): 353–63. http://dx.doi.org/10.1080/10803548.2002.11076535.

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10

Ziegel, Eric R., and H. Rieder. "Robust Statistics, Data Analysis, and Computer Intensive Methods." Technometrics 39, no. 2 (1997): 238. http://dx.doi.org/10.2307/1270930.

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11

Evans, K. J., and Eric W. Noreen. "Computer Intensive Methods for Testing Hypotheses: An Introduction." Journal of the Operational Research Society 41, no. 11 (1990): 1083. http://dx.doi.org/10.2307/2582908.

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12

Smith, Scott M., and Eric W. Noreen. "Computer Intensive Methods for Testing Hypotheses: An Introduction." Journal of Marketing Research 27, no. 3 (1990): 371. http://dx.doi.org/10.2307/3172596.

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13

Flack, Virginia F., and Eric W. Noreen. "Computer Intensive Methods for Testing Hypotheses: An Introduction." Journal of the American Statistical Association 85, no. 410 (1990): 599. http://dx.doi.org/10.2307/2289819.

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14

Militino, Ana Fernández. "Robust statistics, data analysis, and computer intensive methods." Computational Statistics & Data Analysis 24, no. 3 (1997): 373–74. http://dx.doi.org/10.1016/s0167-9473(97)87029-6.

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15

Evans, K. J. "Computer Intensive Methods for Testing Hypotheses: An Introduction." Journal of the Operational Research Society 41, no. 11 (1990): 1083–84. http://dx.doi.org/10.1057/jors.1990.171.

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16

Lenth, Russell V. "Computer Intensive Methods for Testing Hypotheses: An Introduction." Technometrics 32, no. 4 (1990): 453. http://dx.doi.org/10.1080/00401706.1990.10484738.

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17

Beran, R. J. "Computer Intensive Statistical Methods (J. S. Urban Hjorth)." SIAM Review 37, no. 3 (1995): 459–60. http://dx.doi.org/10.1137/1037099.

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18

ARNDT, STEPHAN. "Computer-Intensive Methods for Testing Hypotheses: An Introduction." American Journal of Psychiatry 147, no. 10 (1990): 1373—a—1374. http://dx.doi.org/10.1176/ajp.147.10.1373-a.

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19

Buckland, S. T., and J. S. U. Hjorth. "Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap." Biometrics 50, no. 2 (1994): 586. http://dx.doi.org/10.2307/2533409.

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20

Meinrath, G. "Computer-intensive methods for uncertainty estimation in complex situations." Chemometrics and Intelligent Laboratory Systems 51, no. 2 (2000): 175–87. http://dx.doi.org/10.1016/s0169-7439(00)00066-6.

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21

Worton, B. J., and J. S. U. Hjorth. "Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 3 (1994): 504. http://dx.doi.org/10.2307/2983538.

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22

Lenth, Russell V. "Computer Intensive Statistical Methods: Validation, Model Selection, and Bootstrap." Technometrics 37, no. 4 (1995): 458–59. http://dx.doi.org/10.1080/00401706.1995.10484382.

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23

Pitt, Douglas G., and David P. Kreutzweiser. "Applications of Computer-Intensive Statistical Methods to Environmental Research." Ecotoxicology and Environmental Safety 39, no. 2 (1998): 78–97. http://dx.doi.org/10.1006/eesa.1997.1619.

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24

Saldanha, Richard A., and J. S. U. Hjorth. "Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap." Statistician 44, no. 2 (1995): 288. http://dx.doi.org/10.2307/2348459.

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25

Wasserman, Gary S., and Hassan Mohsen. "Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap." Journal of Quality Technology 27, no. 3 (1995): 268–70. http://dx.doi.org/10.1080/00224065.1995.11979601.

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26

Schmerling, Siegfried. "Computer intensive methods in statistics. Series statistics and computing." Computational Statistics & Data Analysis 17, no. 5 (1994): 602–3. http://dx.doi.org/10.1016/0167-9473(94)90152-x.

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27

Hobbs, N. Thompson. "Introduction to computer-intensive methods of data analysis in biology." Ecology 88, no. 1 (2007): 266–67. http://dx.doi.org/10.1890/0012-9658(2007)88[266:itcmod]2.0.co;2.

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28

Smith, Scott M. "Book Review: Computer Intensive Methods for Testing Hypotheses: An Introduction." Journal of Marketing Research 27, no. 3 (1990): 371–72. http://dx.doi.org/10.1177/002224379002700313.

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29

Coates, David, Neil Doherty, and Alan French. "The new multivariate jungle: Computer intensive methods in database marketing." Journal of Marketing Management 10, no. 1-3 (1994): 207–20. http://dx.doi.org/10.1080/0267257x.1994.9964269.

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30

Robinson, Timothy J. "Introduction to Computer-Intensive Methods of Data Analysis in Biology." American Statistician 61, no. 4 (2007): 371. http://dx.doi.org/10.1198/tas.2007.s145.

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31

Chernoff, Herman. "Simple computer intensive methods for estimating parameters of complex models." Computational Statistics & Data Analysis 12, no. 2 (1991): 159–78. http://dx.doi.org/10.1016/0167-9473(91)90015-t.

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32

Liu, Li Jun. "Research on Computer Network Data Transfer Methods." Advanced Materials Research 926-930 (May 2014): 2807–10. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2807.

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In order to spread across different locations, sharing of computer resources, and ease of use of idle CPU or storage space Resources, there is the concept of grid and grid computing. Data - intensive scientific and engineering applications ( such as seismic data Numerical Simulation of physics, computational mechanics, weather forecast ) needed in a wide area, quick and safe transmission in distributed computing environments Huge amounts of data. So how in a grid environment efficient, reliable, and secure transfer massive files are in the grid computing A study on the key issue. Design and re

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33

Weeks, Melvyn. "Circumventing the Curse of Dimensionality in Applied Work Using Computer Intensive Methods." Economic Journal 105, no. 429 (1995): 520. http://dx.doi.org/10.2307/2235509.

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34

Banos, Arnaud. "Enhancing Mobility Behavior Analysis Using Spatial Interactive Tools and Computer Intensive Methods." Annals of GIS 7, no. 1 (2001): 35–41. http://dx.doi.org/10.1080/10824000109480554.

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35

Sutton, Clifton D. "Computer-Intensive Methods for Tests about the Mean of an Asymmetrical Distribution." Journal of the American Statistical Association 88, no. 423 (1993): 802–10. http://dx.doi.org/10.1080/01621459.1993.10476345.

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36

Butturi-Gomes, Davi, Miguel Petrere Junior, Henrique C. Giacomini, and Paulo De Marco Junior. "Computer intensive methods for controlling bias in a generalized species diversity index." Ecological Indicators 37 (February 2014): 90–98. http://dx.doi.org/10.1016/j.ecolind.2013.10.004.

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37

Bi, Jian. "Computer-intensive methods for sensory data analysis, exemplified by Durbin’s rank test." Food Quality and Preference 20, no. 3 (2009): 195–202. http://dx.doi.org/10.1016/j.foodqual.2008.09.005.

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38

Lynch, Joan K., Peter Fischer, and Sarah F. Green. "Teaching in a Computer-intensive Algebra Curriculum." Mathematics Teacher 82, no. 9 (1989): 688–94. http://dx.doi.org/10.5951/mt.82.9.0688.

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The report of a March 1984 NCTM conference (Corbitt 1985) argued that one of the critical problems facing mathematics education today is the need for curricula and instructional methods that reflect the influence of computing on mathematics and its teaching. The conference report suggested that emerging technology would make possible a significant shift in curricular priorities and in patterns of classroom organization. The curricular emphasis could shift from ma nipulative skills to concepts, relationships, structures, and problem solving. The instructional emphasis could shift from teacher p

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39

Mode, Charles J., and Candace K. Sleeman. "An algorithmic synthesis of the deterministic and stochastic paradigms via computer intensive methods." Mathematical Biosciences 180, no. 1-2 (2002): 115–26. http://dx.doi.org/10.1016/s0025-5564(02)00126-8.

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40

Ver Hoef, Jay M. "Practical considerations for experimental designs of spatially autocorrelated data using computer intensive methods." Statistical Methodology 9, no. 1-2 (2012): 172–84. http://dx.doi.org/10.1016/j.stamet.2011.04.005.

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41

Kemp, A. W. "Computer-Intensive Methods of Data Analysis in Biology edited by D. A. Roff." Biometrics 63, no. 2 (2007): 625–26. http://dx.doi.org/10.1111/j.1541-0420.2006.00787_2.x.

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42

Cohen, Ayala, Udi Makov, and Ruth Boneh. "Computer intensive methods for inference on parameters of complex models — A Bayesian alternative." Journal of Statistical Planning and Inference 47, no. 1-2 (1995): 79–94. http://dx.doi.org/10.1016/0378-3758(94)00123-d.

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43

Romero, Martín. "Introduction to Computer-Intensive Methods of Data Analysis in Biology by Derek A. Roff." Bulletin of Mathematical Biology 69, no. 8 (2007): 2773–74. http://dx.doi.org/10.1007/s11538-007-9216-y.

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44

Chernick, Michael R. "Guest Editor's Note: Computer-Intensive Methods with Emphasis On Bootstrap and Medical Research Applications." Journal of Biopharmaceutical Statistics 21, no. 6 (2011): 1049–51. http://dx.doi.org/10.1080/10543406.2011.607734.

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45

Clémençon, Stéphan, Anthony Cousien, Miraine Dávila Felipe, and Viet Chi Tran. "On computer-intensive simulation and estimation methods for rare-event analysis in epidemic models." Statistics in Medicine 34, no. 28 (2015): 3696–713. http://dx.doi.org/10.1002/sim.6596.

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46

Poblet, Marta, Esteban García-Cuesta, and Pompeu Casanovas. "Crowdsourcing roles, methods and tools for data-intensive disaster management." Information Systems Frontiers 20, no. 6 (2017): 1363–79. http://dx.doi.org/10.1007/s10796-017-9734-6.

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47

Kataev, M. Yu, R. K. Karpov, and K. A. Laminsky. "Software system for detecting brick defects based on computer vision methods." Proceedings of Tomsk State University of Control Systems and Radioelectronics 24, no. 1 (2021): 62–67. http://dx.doi.org/10.21293/1818-0442-2021-24-1-62-67.

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For industrial enterprises, an important area of activity is the use of non-destructive testing in assessing product quality. One of the most developed approaches in practice are optical methods (visible or infrared spectral regions) and associated algorithmic approaches to computer vision. Today a large number of industries use a manual method for detecting defects, which is labor-intensive, slow and subjective. The article presents a description of an automated optical control system to detect imperfections in bricks on a conveyor belt during their production. The optical control is based on

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48

Vergeest, J. S. M., I. Horváth, and P. Li. "Knowledge-Intensive Tools and Methods of Collaboration in Engineering." Concurrent Engineering 11, no. 2 (2003): 81. http://dx.doi.org/10.1177/1063293x03035428.

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49

Maiti, Tapabrata, and Pushpal Mukhopadhyay. "Comparison of Statistical Classification Methods Based on a Prostate Cancer Study." Calcutta Statistical Association Bulletin 57, no. 3-4 (2005): 219–38. http://dx.doi.org/10.1177/0008068320050306.

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Prostate cancer is one of the most common cancers in American men. Management of prostate cancer depends on its stage, because only cancers that are confined to the organ of origin are potentially curable by radical prostatectomy. In this article we have considered different statistical methods to predict the probabilities of nonorgan confined prostate cancer based on its clinical stage. Modern computer intensive methods such as bagging, neural networks and support vector machines are compared to more classical methods such as linear, quadratic and logistic discrimination and less computer in

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50

MATTFELDT, TORSTEN. "Prediction of the variance of stereological volume estimates from systematic sections using computer-intensive methods." Journal of Microscopy 222, no. 3 (2006): 166–76. http://dx.doi.org/10.1111/j.1365-2818.2006.01584.x.

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