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Journal articles on the topic 'Discrete data models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Kianifard, Farid. "Models for Discrete Data." Technometrics 42, no. 3 (August 2000): 313–14. http://dx.doi.org/10.1080/00401706.2000.10486061.

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2

Williamson, John. "Models for Discrete Longitudinal Data." Journal of the American Statistical Association 101, no. 475 (September 2006): 1307. http://dx.doi.org/10.1198/jasa.2006.s117.

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3

Karlsson, Andreas. "Models for Discrete Longitudinal Data." Biometrics 62, no. 2 (June 2006): 628. http://dx.doi.org/10.1111/j.1541-0420.2006.00589_5.x.

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4

Paule, Ines, Pascal Girard, Gilles Freyer, and Michel Tod. "Pharmacodynamic Models for Discrete Data." Clinical Pharmacokinetics 51, no. 12 (October 17, 2012): 767–86. http://dx.doi.org/10.1007/s40262-012-0014-9.

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5

Bruni, Renato. "Discrete models for data imputation." Discrete Applied Mathematics 144, no. 1-2 (November 2004): 59–69. http://dx.doi.org/10.1016/j.dam.2004.04.004.

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6

Madigan, David, Jeremy York, and Denis Allard. "Bayesian Graphical Models for Discrete Data." International Statistical Review / Revue Internationale de Statistique 63, no. 2 (August 1995): 215. http://dx.doi.org/10.2307/1403615.

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7

Teugels, Jozef L., and Johan Van Horebeek. "Generalized graphical models for discrete data." Statistics & Probability Letters 38, no. 1 (May 1998): 41–47. http://dx.doi.org/10.1016/s0167-7152(97)00152-1.

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8

Karlis, Dimitris. "Book Review: Models for discrete data." Statistical Methods in Medical Research 10, no. 5 (October 2001): 367–68. http://dx.doi.org/10.1177/096228020101000507.

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9

Shinbrot, Marvin. "Discrete velocity models with small data." Meccanica 22, no. 1 (March 1987): 38–40. http://dx.doi.org/10.1007/bf01560124.

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10

Bouguila, Nizar, and Walid ElGuebaly. "Discrete data clustering using finite mixture models." Pattern Recognition 42, no. 1 (January 2009): 33–42. http://dx.doi.org/10.1016/j.patcog.2008.06.022.

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11

Newman, Jeffrey, Mark E. Ferguson, and Laurie A. Garrow. "Estimating Discrete Choice Models with Incomplete Data." Transportation Research Record: Journal of the Transportation Research Board 2302, no. 1 (January 2012): 130–37. http://dx.doi.org/10.3141/2302-14.

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12

Woodbury, Max A., Kenneth G. Manton, and H. Dennis Tolley. "Convex Models of High Dimensional Discrete Data." Annals of the Institute of Statistical Mathematics 49, no. 2 (June 1997): 371–93. http://dx.doi.org/10.1023/a:1003175232300.

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13

Xie, Yu. "Log-Multiplicative Models for Discrete-Time, Discrete-Covariate Event-History Data." Sociological Methodology 24 (1994): 301. http://dx.doi.org/10.2307/270986.

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14

Fan, Tong Rang, Yong Bin Zhao, and Lan Wang. "A Discrete Data Fitting Models Fusing Genetic Algorithm." Advanced Materials Research 267 (June 2011): 427–32. http://dx.doi.org/10.4028/www.scientific.net/amr.267.427.

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To address problems of Least squares method (LSM) fitting curves in application domains, the essay attempts to build a new model by using LMS (Least Median Squares) to analyze the error points, and pretreating the dynamic measuring errors and then getting the fitting curves of testing data. This model is used for electromotor parameters testing which includes load testing and unload testing. Experiments show that the model can erase the influence of outline points, while improving the effects of data curve fitting and reflecting the characteristic of the motor, provide more accurate data fitting curve with small sample data that is in discrete distribution compared with Least squares method.
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15

Forcina, Antonio. "Smoothness of conditional independence models for discrete data." Journal of Multivariate Analysis 106 (April 2012): 49–56. http://dx.doi.org/10.1016/j.jmva.2011.11.009.

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16

Lee, Lung-Fei. "Non-parametric testing of discrete panel data models." Journal of Econometrics 34, no. 1-2 (January 1987): 147–77. http://dx.doi.org/10.1016/0304-4076(87)90071-6.

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17

Moses, Tim. "Underlying Distributions in Loglinear Models of Discrete Data." Journal of Modern Applied Statistical Methods 11, no. 1 (May 1, 2012): 2–23. http://dx.doi.org/10.22237/jmasm/1335844860.

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18

Edwards, David, and Smitha Ankinakatte. "Context-specific graphical models for discrete longitudinal data." Statistical Modelling: An International Journal 15, no. 4 (December 9, 2014): 301–25. http://dx.doi.org/10.1177/1471082x14551248.

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19

Rapisarda, P. "Discrete Roesser state models from 2D frequency data." Multidimensional Systems and Signal Processing 30, no. 2 (March 31, 2018): 591–610. http://dx.doi.org/10.1007/s11045-018-0572-6.

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20

Kalwani, Manohar U., Robert J. Meyer, and Donald G. Morrison. "Benchmarks for Discrete Choice Models." Journal of Marketing Research 31, no. 1 (February 1994): 65–75. http://dx.doi.org/10.1177/002224379403100106.

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In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.
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21

Chernyshenko, Serge V. "Continuous Description of Discrete Biological Data." International Journal of Applied Research in Bioinformatics 9, no. 1 (January 2019): 36–49. http://dx.doi.org/10.4018/ijarb.2019010103.

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The applicability of differential equations to description of integer values dynamics in bio-informatics is investigated. It is shown that a differential model may be interpreted as a continuous analogue of a stochastic flow. The method of construction of a quasi-Poisson flow on the base of multi-dimension differential equations is proposed. Mathematical correctness of the algorithm is proven. The system has been studied by a computer simulation and a discrete nature of processes has been taken into account. The proposed schema has been applied to the classical Volterra's models, which are widely used for description of biological systems. It has been demonstrated that although behaviour of discrete and continuous models is similar, some essential qualitative and quantitative differences in their dynamics take place.
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22

Langeheine, Rolf. "Manifest and Latent Markov Chain Models for Categorical Panel Data." Journal of Educational Statistics 13, no. 4 (December 1988): 299–312. http://dx.doi.org/10.3102/10769986013004299.

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The starting point of this paper is a 3 × 3 × 3 table of repeated behavior ratings of children, which has been previously analyzed by Plewis (1981) using manifest discrete time and continuous time Markov chain models. Potential reasons for the ubiquitous misfit of the manifest discrete time Markov chain model are outlined. It is proposed, instead, to make use of more recent developments in latent discrete time Markov chain modeling that simultaneously address the main problems of heterogeneity, measurement error, stationarity, and order effects.
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23

Kibria, B. M. Golam. "Applications of some discrete regression models for count data." Pakistan Journal of Statistics and Operation Research 2, no. 1 (January 1, 2006): 1. http://dx.doi.org/10.18187/pjsor.v2i1.81.

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24

Metcalf, C. J. E., J. Lessler, P. Klepac, A. Morice, B. T. Grenfell, and O. N. Bjørnstad. "Structured models of infectious disease: Inference with discrete data." Theoretical Population Biology 82, no. 4 (December 2012): 275–82. http://dx.doi.org/10.1016/j.tpb.2011.12.001.

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25

Espeland, Mark A. "A general class of models for discrete multivariate data." Communications in Statistics - Simulation and Computation 15, no. 2 (January 1986): 405–24. http://dx.doi.org/10.1080/03610918608812515.

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26

Kristensen, Niels Rode, Henrik Madsen, and Sten Bay Jørgensen. "Identification of continuous time models using discrete time data." IFAC Proceedings Volumes 36, no. 16 (September 2003): 615–20. http://dx.doi.org/10.1016/s1474-6670(17)34829-2.

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27

Schmittmann, Verena D., Conor V. Dolan, Han L. J. van der Maas, and Michael C. Neale. "Discrete Latent Markov Models for Normally Distributed Response Data." Multivariate Behavioral Research 40, no. 4 (October 2005): 461–88. http://dx.doi.org/10.1207/s15327906mbr4004_4.

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28

Chalita, Liciana V. A. S., Enrico A. Colosimo, and Clarice G. B. Demétrio. "LIKELIHOOD APPROXIMATIONS AND DISCRETE MODELS FOR TIED SURVIVAL DATA." Communications in Statistics - Theory and Methods 31, no. 7 (July 23, 2002): 1215–29. http://dx.doi.org/10.1081/sta-120004920.

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29

Honore, Bo E., and Ekaterini Kyriazidou. "Panel Data Discrete Choice Models with Lagged Dependent Variables." Econometrica 68, no. 4 (July 2000): 839–74. http://dx.doi.org/10.1111/1468-0262.00139.

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30

Peters, J., D. Janzing, and B. Scholkopf. "Causal Inference on Discrete Data Using Additive Noise Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 12 (December 2011): 2436–50. http://dx.doi.org/10.1109/tpami.2011.71.

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31

Esfandiari, Mahmood, Ashkan Hafezalkotob, Kaveh Khalili-Damghani, and Mohammad Amirkhan. "Robust two-stage DEA models under discrete uncertain data." International Journal of Management Science and Engineering Management 12, no. 3 (November 2, 2016): 216–24. http://dx.doi.org/10.1080/17509653.2016.1224132.

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32

Hong, Han, Weiming Li, and Boyu Wang. "Estimation of dynamic discrete models from time aggregated data." Journal of Econometrics 188, no. 2 (October 2015): 435–46. http://dx.doi.org/10.1016/j.jeconom.2015.03.009.

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33

Pasanisi, Alberto, Shuai Fu, and Nicolas Bousquet. "Estimating discrete Markov models from various incomplete data schemes." Computational Statistics & Data Analysis 56, no. 9 (September 2012): 2609–25. http://dx.doi.org/10.1016/j.csda.2012.02.027.

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34

Celeux, Gilles, and Gérard Govaert. "Clustering criteria for discrete data and latent class models." Journal of Classification 8, no. 2 (December 1991): 157–76. http://dx.doi.org/10.1007/bf02616237.

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35

Mendes, Eduardo M. A. M., and S. A. Billings. "On Identifying Global Nonlinear Discrete Models from Chaotic Data." International Journal of Bifurcation and Chaos 07, no. 11 (November 1997): 2593–601. http://dx.doi.org/10.1142/s0218127497001758.

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This paper investigates the identification of global discrete nonlinear models from chaotic data. It is shown that when chaotic data from a nonlinear system do not contain enough information about all the fixed points, the usual model selection procedures can select different local nonlinear models.
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36

Chen, Yi-Chun, Tim A. Wheeler, and Mykel J. Kochenderfer. "Learning Discrete Bayesian Networks from Continuous Data." Journal of Artificial Intelligence Research 59 (June 22, 2017): 103–32. http://dx.doi.org/10.1613/jair.5371.

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Learning Bayesian networks from raw data can help provide insights into the relationships between variables. While real data often contains a mixture of discrete and continuous-valued variables, many Bayesian network structure learning algorithms assume all random variables are discrete. Thus, continuous variables are often discretized when learning a Bayesian network. However, the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting models. This paper introduces a principled Bayesian discretization method for continuous variables in Bayesian networks with quadratic complexity instead of the cubic complexity of other standard techniques. Empirical demonstrations show that the proposed method is superior to the established minimum description length algorithm. In addition, this paper shows how to incorporate existing methods into the structure learning process to discretize all continuous variables and simultaneously learn Bayesian network structures.
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37

Zhu, J., L. S. Shieh, and R. E. Yates. "Fitting continuous-time and discrete-time models using discrete-time data and their applications." Applied Mathematical Modelling 9, no. 2 (April 1985): 93–98. http://dx.doi.org/10.1016/0307-904x(85)90119-2.

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38

McConnell, K. E. "Models for referendum data: The structure of discrete choice models for contingent valuation." Journal of Environmental Economics and Management 18, no. 1 (January 1990): 19–34. http://dx.doi.org/10.1016/0095-0696(90)90049-5.

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39

Robertson, A. N., K. C. Park, and K. F. Alvin. "Identification of Structural Dynamics Models Using Wavelet-Generated Impulse Response Data." Journal of Vibration and Acoustics 120, no. 1 (January 1, 1998): 261–66. http://dx.doi.org/10.1115/1.2893815.

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This paper addresses the use of discrete wavelet transforms for the identification of structural dynamics models. First, the discrete temporal impulse response functions are obtained from vibration records by the discrete wavelet transform (DWT). They are then utilized for system realizations. From the realized state space models, structural modes, mode shapes and damping parameters are extracted. Attention has been focused on a careful comparison of the present DWT system identification approach to the FFT-based approach. Numerical examples demonstrate that the present DWT-based structural system identification procedure is a serious alternative to the FFT-based procedure, and outperforms FFT methods for narrow frequency-band inputs.
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40

Tsachouridis, Vassilios A., and Thomas Scherer. "Data centre adaptive numerical temperature models." Transactions of the Institute of Measurement and Control 40, no. 6 (March 22, 2017): 1911–26. http://dx.doi.org/10.1177/0142331217694684.

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Research results are presented regarding the online derivation of temperature state space models for air cooled data centre rooms. Using exclusively real time temperature measurements from indoor sensors, filter algorithms are programmed for the numerical computation of the parameters of discrete time varying state space models. These control oriented models are adaptive and can predict the temperature distribution across data centre rooms where air-conditioning units are used to compensate heat loads generated by the computing equipment. The research has been conducted for the European Union project GENiC and the adopted approach has been tested and validated on a real data centre facility.
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41

Denney, Dennis. "Fracture- and Production- Data Integration With Discrete- Fracture-Network Models." Journal of Petroleum Technology 52, no. 12 (December 1, 2000): 60–61. http://dx.doi.org/10.2118/1200-0060-jpt.

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42

Sur, Pragya, Galit Shmueli, Smarajit Bose, and Paromita Dubey. "Modeling Bimodal Discrete Data Using Conway-Maxwell-Poisson Mixture Models." Journal of Business & Economic Statistics 33, no. 3 (July 3, 2015): 352–65. http://dx.doi.org/10.1080/07350015.2014.949343.

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43

GOLDSTEIN, HARVEY. "Nonlinear multilevel models, with an application to discrete response data." Biometrika 78, no. 1 (1991): 45–51. http://dx.doi.org/10.1093/biomet/78.1.45.

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44

Gørgens, Tue, and Dean Hyslop. "Equivalent representations of discrete-time two-state panel data models." Economics Letters 163 (February 2018): 65–67. http://dx.doi.org/10.1016/j.econlet.2017.12.003.

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45

Zhenshan, Lin, Zhu Yanyu, and Deng Ziwang. "Experiments of reconstructing discrete atmospheric dynamic models from data (I)." Advances in Atmospheric Sciences 12, no. 1 (March 1995): 121–25. http://dx.doi.org/10.1007/bf02661295.

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46

Haertel, Edward H. "Continuous and discrete latent structure models for item response data." Psychometrika 55, no. 3 (September 1990): 477–94. http://dx.doi.org/10.1007/bf02294762.

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47

Webel, Karsten. "G. Molenberghs and G. Verbeke: Models for Discrete Longitudinal Data." AStA Advances in Statistical Analysis 91, no. 2 (July 13, 2007): 223–24. http://dx.doi.org/10.1007/s10182-007-0029-y.

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48

AGUIRRE, LUIS ANTONIO. "CHAOTIFICATION OF DISCRETE SYSTEMS BASED ON MODELS IDENTIFIED FROM DATA." International Journal of Bifurcation and Chaos 16, no. 01 (January 2006): 185–90. http://dx.doi.org/10.1142/s0218127406014708.

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This work discusses the chaotification of nonchaotic discrete systems based on a procedure that requires the jacobian matrix of the uncontrolled dynamics. The aim is to verify if chaotification is still effective when the jacobian is analytically obtained from identified models (data-driven models) rather than assumed known. This study, that includes two numerical examples, shows that chaotification can be achieved using nonlinear models. Several other issues concerning modeling and the control effort required for chaotification are briefly discussed.
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49

Carro, Jesus M. "Estimating dynamic panel data discrete choice models with fixed effects." Journal of Econometrics 140, no. 2 (October 2007): 503–28. http://dx.doi.org/10.1016/j.jeconom.2006.07.023.

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50

Ferber, Kyle, and Kellie J. Archer. "Modeling Discrete Survival Time Using Genomic Feature Data." Cancer Informatics 14s2 (January 2015): CIN.S17275. http://dx.doi.org/10.4137/cin.s17275.

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Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment.
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