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Relevant bibliographies by topics / Discrete data analysis / Journal articles

Journal articles on the topic 'Discrete data analysis'

To see the other types of publications on this topic, follow the link: Discrete data analysis.

Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Booth, James G. "Exact Analysis of Discrete Data." Journal of the American Statistical Association 102, no. 478 (June 2007): 764–65. http://dx.doi.org/10.1198/jasa.2007.s193.

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2

Cooper, Philip J., and Mikel Aickin. "Linear Statistical Analysis of Discrete Data." Statistician 35, no. 3 (1986): 400. http://dx.doi.org/10.2307/2987766.

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3

Morris, Pamela, T. J. Santner, and D. E. Duffy. "The Statistical Analysis of Discrete Data." Mathematical Gazette 74, no. 470 (December 1990): 410. http://dx.doi.org/10.2307/3618176.

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4

Greenhouse, Samuel W., Thomas J. Santner, and Diane E. Duffy. "The Statistical Analysis of Discrete Data." Journal of the American Statistical Association 86, no. 415 (September 1991): 827. http://dx.doi.org/10.2307/2290426.

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5

Kenward, M. G., T. J. Santner, and D. E. Duffy. "The Statistical Analysis of Discrete Data." Biometrics 47, no. 3 (September 1991): 1200. http://dx.doi.org/10.2307/2532678.

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6

Wasserman, Stanley, and Dawn Iacobucci. "Statistical analysis of discrete relational data." British Journal of Mathematical and Statistical Psychology 39, no. 1 (May 1986): 41–64. http://dx.doi.org/10.1111/j.2044-8317.1986.tb00844.x.

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7

Gart, John J. "Linear Statistical Analysis of Discrete Data." Technometrics 27, no. 3 (August 1985): 316–17. http://dx.doi.org/10.1080/00401706.1985.10488058.

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8

Simpson, Douglas G. "The Statistical Analysis of Discrete Data." Technometrics 33, no. 2 (May 1991): 242. http://dx.doi.org/10.1080/00401706.1991.10484818.

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9

Pedersen, L. B. "Geophysical data analysis: Discrete inverse theory." Physics of the Earth and Planetary Interiors 62, no. 3-4 (January 1990): 343. http://dx.doi.org/10.1016/0031-9201(90)90180-6.

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10

Beckerle, John C. "Geophysical data analysis: Discrete inverse theory." Tectonophysics 124, no. 3-4 (April 1986): 366–67. http://dx.doi.org/10.1016/0040-1951(86)90212-x.

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11

Iyer, R., Thomas J. Santner, and Diane E. Duffy. "The Statistical Analysis of Discrete Data." Statistician 40, no. 3 (1991): 345. http://dx.doi.org/10.2307/2348290.

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12

Bancroft, Diccon, and Mikel Aickin. "Linear Statistical Analysis of Discrete Data." Journal of the American Statistical Association 81, no. 393 (March 1986): 252. http://dx.doi.org/10.2307/2288002.

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13

Beckett, L., and P. Diaconis. "Spectral Analysis for Discrete Longitudinal Data." Advances in Mathematics 103, no. 1 (January 1994): 107–28. http://dx.doi.org/10.1006/aima.1994.1002.

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14

Panov, V. G. "ANALYSIS OF DISCRETE DATA. BASIC NOTIONS." Траектория исследований – человек, природа, технологии, no. 3 (2022): 105–41. http://dx.doi.org/10.56564/27825264_2022_3_105.

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15

Ditzian, Z. "Moduli of smoothness using discrete data." Journal of Approximation Theory 49, no. 2 (February 1987): 115–29. http://dx.doi.org/10.1016/0021-9045(87)90082-7.

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16

Friedman, Avner, Biao Ou, and David S. Ross. "Crystal precipitation with discrete initial data." Journal of Mathematical Analysis and Applications 137, no. 2 (February 1989): 576–90. http://dx.doi.org/10.1016/0022-247x(89)90264-3.

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17

Slatkin, Montgomery, Wayne P. Maddison, and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Zoology 40, no. 2 (June 1991): 248. http://dx.doi.org/10.2307/2992265.

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18

Chase, Gary A., and Bruce S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Journal of the American Statistical Association 86, no. 413 (March 1991): 248. http://dx.doi.org/10.2307/2289745.

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19

Feytmans, E., and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Biometrics 47, no. 3 (September 1991): 1205. http://dx.doi.org/10.2307/2532683.

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20

Morton, N. E. "Genetic Data Analysis. Methods for Discrete Population Genetic Data." Journal of Medical Genetics 29, no. 3 (March 1, 1992): 216. http://dx.doi.org/10.1136/jmg.29.3.216.

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21

Slatkin, M., and W. P. Maddison. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Biology 40, no. 2 (June 1, 1991): 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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22

Stephens, David, and Martin Crowder. "Bayesian analysis of discrete time warranty data." Journal of the Royal Statistical Society: Series C (Applied Statistics) 53, no. 1 (January 2004): 195–217. http://dx.doi.org/10.1111/j.1467-9876.2004.00435.x.

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23

Madsen, L., and Y. Fang. "Joint Regression Analysis for Discrete Longitudinal Data." Biometrics 67, no. 3 (October 29, 2010): 1171–75. http://dx.doi.org/10.1111/j.1541-0420.2010.01494.x.

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24

Bennink, Margot, Marcel A. Croon, and Jeroen K. Vermunt. "Micro–Macro Multilevel Analysis for Discrete Data." Sociological Methods & Research 42, no. 4 (September 30, 2013): 431–57. http://dx.doi.org/10.1177/0049124113500479.

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25

Zerafat Angiz L., Majid, Ali Emrouznejad, and Adli Mustafa. "Fuzzy data envelopment analysis: A discrete approach." Expert Systems with Applications 39, no. 3 (February 2012): 2263–69. http://dx.doi.org/10.1016/j.eswa.2011.07.118.

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26

Ross-Ibarra, Jeffrey. "Genetic Data Analysis II. Methods for Discrete Population Genentic Data." Economic Botany 56, no. 2 (April 2002): 216. http://dx.doi.org/10.1663/0013-0001(2002)056[0216:gdaimf]2.0.co;2.

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27

Isaman, Deanna J. M., Jacob Barhak, and Wen Ye. "Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data." Statistics in Medicine 28, no. 16 (May 19, 2009): 2095–115. http://dx.doi.org/10.1002/sim.3599.

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28

Zhang, Bao, Chenpeng Feng, Min Yang, Jianhui Xie, and Ya Chen. "Bounded and discrete data in data envelopment analysis with assurance regions." Journal of Modelling in Management 15, no. 3 (February 20, 2020): 1017–36. http://dx.doi.org/10.1108/jm2-09-2019-0225.

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Purpose The purpose of this paper is to evaluate design performance of 51 gear shaping machines by using data envelopment analysis (DEA). Design/methodology/approach Existing studies extend traditional DEA by handling bounded and discrete data based on envelopment models. However, value judgment is usually neglected and fail to be incorporated in these envelopment models. In many cases, there is a need for prior preferences. Using existing DEA approaches as a backdrop, the current paper presents a methodology for incorporating assurance region (AR) restrictions into DEA with bounded and discrete data, i.e. the assurance region bounded discrete (AR-BD) DEA model. Then, the AR-BD DEA model is combined with a context-dependent DEA to obtain an efficiency stratification. Findings The authors examine different AR restrictions and calculate efficiency scores of five scenarios of AR restrictions by using the proposed AR-BD DEA model. It shows that AR restrictions have a great impact on the efficiency scores. The authors also identify nine efficient frontiers in total. For each decision-making unit, it could set benchmarks and improve its performance based on each higher efficient frontier. Originality/value This paper first evaluates efficiency of gear shaping machines by considering different (bounded and discrete) variable types of data and including AR restrictions. The AR-BD DEA model and context-dependent AR-BD DEA model proposed in this paper further enrich the DEA theory. The findings in this paper certainly provide useful information for both producers and consumers to make smart decisions.

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29

Zeger, Scott L., and Kung-Yee Liang. "Longitudinal Data Analysis for Discrete and Continuous Outcomes." Biometrics 42, no. 1 (March 1986): 121. http://dx.doi.org/10.2307/2531248.

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30

Baglivo, Jenny, Donald Olivier, and Marcello Pagano. "Analysis of discrete data: Rerandomization methods and complexity." Computational Statistics & Data Analysis 16, no. 2 (August 1993): 175–84. http://dx.doi.org/10.1016/0167-9473(93)90112-7.

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31

Moreno, E., F. J. Vázquez-Polo, and M. A. Negrín. "Objective Bayesian meta-analysis for sparse discrete data." Statistics in Medicine 33, no. 21 (April 8, 2014): 3676–92. http://dx.doi.org/10.1002/sim.6163.

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32

Faddy, M. J. "Markov process modelling and analysis of discrete data." Applied Stochastic Models and Data Analysis 13, no. 3-4 (September 1997): 217–23. http://dx.doi.org/10.1002/(sici)1099-0747(199709/12)13:3/4<217::aid-asm315>3.0.co;2-7.

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33

Burge, John, Terran Lane, Hamilton Link, Shibin Qiu, and Vincent P. Clark. "Discrete dynamic Bayesian network analysis of fMRI data." Human Brain Mapping 30, no. 1 (January 2009): 122–37. http://dx.doi.org/10.1002/hbm.20490.

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34

Isaman, D. J. M., Jacob Barhak, and W. Ye. "Erratum: Indirect estimation of a discrete-state discrete-time model using secondary data analysis of regression data." Statistics in Medicine 29, no. 10 (February 9, 2010): 1158. http://dx.doi.org/10.1002/sim.3855.

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35

Ragozin, David L. "The Discrete k-Functional and Spline Smoothing of Noisy Data." SIAM Journal on Numerical Analysis 22, no. 6 (December 1985): 1243–54. http://dx.doi.org/10.1137/0722077.

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36

Chen, Hongli, and Yuzheng Gao. "Behavior Data Analysis of English Learners Based on Discrete Dynamic System Modeling Method." Computational Intelligence and Neuroscience 2022 (September 2, 2022): 1–8. http://dx.doi.org/10.1155/2022/5409571.

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With the rapid development of computer science, there are more and more kinds of discrete dynamic systems. Computer integrated system CIMS, network communication database administrator system, and human behavior analysis system are all discrete dynamic systems. At present, many researchers have studied by adding human behavior data to discrete dynamic systems. This paper aims to study the behavior data of English learners by using the discrete dynamic modeling technology of complex systems and the discrete dynamic system modeling method of Petri nets. By adding the behavior data of learners to the discrete dynamic system of fuzzy Petri nets, the system is diagnosed and optimized. The experimental results show that the complex discrete dynamic system in this paper has achieved good experimental results according to the performance indicators selected in theory. Based on the combination of the above technologies and systems, the fuzzy Petri net discrete dynamic system studied in this paper improves the processing speed of English learners' behavior data.

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37

Chen, Zhihau, Alex Tretyakov, Hideki Takayasu, and Nobukazu Nakasato. "Spectral Analysis of Multichannel Meg Data." Fractals 06, no. 04 (December 1998): 395–400. http://dx.doi.org/10.1142/s0218348x98000432.

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We use the Discrete Wavelet Transform in order to study the power spectrum of data obtained in magnetoencephalographical measurements. α-wave phenomenon is found to occur independently 1/fβ noise, which is present over almost all channels. the β value is close to 1.

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38

Ehler, Martin, and Frank Filbir. "ε-Coverings of Hölder-Zygmund Type Spaces on Data-Defined Manifolds." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/402918.

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We first determine the asymptotes of theε-covering numbers of Hölder-Zygmund type spaces on data-defined manifolds. Secondly, a fully discrete and finite algorithmic scheme is developed providing explicitε-coverings whose cardinality is asymptotically near theε-covering number. Given an arbitrary Hölder-Zygmund type function, the nearby center of a ball in theε-covering can also be computed in a discrete finite fashion.

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39

Diday, Edwin. "Symbolic Data Analysis." Electronic Notes in Discrete Mathematics 2 (April 1999): 189–98. http://dx.doi.org/10.1016/s1571-0653(04)00047-2.

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40

Lee, Minjung, Eric J. Feuer, and Jason P. Fine. "On the analysis of discrete time competing risks data." Biometrics 74, no. 4 (April 17, 2018): 1468–81. http://dx.doi.org/10.1111/biom.12881.

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41

Amirteimoori, Alireza, and Sohrab Kordrostami. "Data envelopment analysis with discrete-valued inputs and outputs." Expert Systems 31, no. 4 (July 17, 2013): 335–42. http://dx.doi.org/10.1111/exsy.12045.

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42

Fischer, Manfred M., and Peter Nijkamp. "Developments in Explanatory Discrete Spatial Data and Choice Analysis." Progress in Human Geography 9, no. 4 (December 1985): 515–51. http://dx.doi.org/10.1177/030913258500900403.

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43

Lee, S., B. L. Ibey, W. Xu, M. A. Wilson, M. N. Ericson, and G. L. Cote. "Processing of Pulse Oximeter Data Using Discrete Wavelet Analysis." IEEE Transactions on Biomedical Engineering 52, no. 7 (July 2005): 1350–52. http://dx.doi.org/10.1109/tbme.2005.847538.

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44

Gupta, Ramesh C., S. Z. Sim, and S. H. Ong. "Analysis of discrete data by Conway–Maxwell Poisson distribution." AStA Advances in Statistical Analysis 98, no. 4 (February 23, 2014): 327–43. http://dx.doi.org/10.1007/s10182-014-0226-4.

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45

Wang, Zhuo, and Derong Liu. "Data-Based Characteristics Analysis for Linear Discrete-Time Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 4993–98. http://dx.doi.org/10.3182/20140824-6-za-1003.00248.

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46

Gubbins, David. "A review of: “Geophysical data analysis: Discrete inverse theory”." Geophysical & Astrophysical Fluid Dynamics 32, no. 3-4 (July 1985): 336–39. http://dx.doi.org/10.1080/03091928508208791.

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47

Xue, Xiaonan, and Ron Brookmeyer. "Regression analysis of discrete time survival data under heterogeneity." Statistics in Medicine 16, no. 17 (September 15, 1997): 1983–93. http://dx.doi.org/10.1002/(sici)1097-0258(19970915)16:17<1983::aid-sim628>3.0.co;2-3.

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48

Ndlovu, Bonginkosi, Sileshi Melesse, and Temesgen Zewotir. "A regression analysis of discrete time competing risks data using a vertical model approach." South African Statistical Journal 56, no. 1 (2022): 21–36. http://dx.doi.org/10.37920/sasj.2022.56.1.2.

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Over the years, the standard regression analysis method for discrete time competing risks data has been to model the data with discrete time cause-specific hazards. While a few continuous time competing risks models have been proposed in the literature, it is a well documented fact that these models are not appropriate for application in discrete time as is. The vertical regression model of Nicolaie et al. (2010) is the latest of these continuous time competing risks models. We reformulate this regression model for the purpose of application in discrete time. We demonstrate that the proposed model can easily be implemented by using existing software for discrete time models. We apply the proposed model together with some of the existing discrete time models to real discrete time competing risks data and find that the proposed model and these models compare favourably.

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49

Jorde, L. B. "Genetic Data Analysis: Methods for Discrete Population Genetic Data. Bruce S. Weir." Quarterly Review of Biology 66, no. 4 (December 1991): 488–89. http://dx.doi.org/10.1086/417362.

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50

Teugels, J. L., and J. Van Horebeek. "Algebraic Descriptions of Nominal Multivariate Discrete Data." Journal of Multivariate Analysis 67, no. 2 (November 1998): 203–26. http://dx.doi.org/10.1006/jmva.1998.1764.

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