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Journal articles on the topic 'Continuous Time Modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Pintelon, R., J. Schoukens, and Y. Rolain. "Box-Jenkins Continuous-Time Modeling." IFAC Proceedings Volumes 33, no. 15 (2000): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)39749-5.

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2

Pintelon, R., J. Schoukens, and Y. Rolain. "Box–Jenkins continuous-time modeling." Automatica 36, no. 7 (2000): 983–91. http://dx.doi.org/10.1016/s0005-1098(00)00002-9.

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3

Driver, Charles C., and Manuel C. Voelkle. "Hierarchical Bayesian continuous time dynamic modeling." Psychological Methods 23, no. 4 (2018): 774–99. http://dx.doi.org/10.1037/met0000168.

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4

Oud, Johan H. L., Henk Folmer, Roberto Patuelli, and Peter Nijkamp. "Continuous-Time Modeling with Spatial Dependence." Geographical Analysis 44, no. 1 (2012): 29–46. http://dx.doi.org/10.1111/j.1538-4632.2011.00834.x.

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5

Pintelon, Rik, Johan Schoukens, and Patrick Guillaume. "Continuous-Time Noise Modeling From Sampled Data." IEEE Transactions on Instrumentation and Measurement 55, no. 6 (2006): 2253–58. http://dx.doi.org/10.1109/tim.2006.884131.

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6

Park, Jong-Jin, Gyoo-Seok Choi, In-Ku Park, and Jeong-Jin Kang. "Digital Modeling of a Time delayed Continuous-Time System." Journal of the Institute of Webcasting, Internet and Telecommunication 12, no. 1 (2012): 211–16. http://dx.doi.org/10.7236/jiwit.2012.12.1.211.

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7

Niu, Mu, Paul G. Blackwell, and Anna Skarin. "Modeling interdependent animal movement in continuous time." Biometrics 72, no. 2 (2016): 315–24. http://dx.doi.org/10.1111/biom.12454.

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8

Shain, Cory, and William Schuler. "Continuous-time deconvolutional regression for psycholinguistic modeling." Cognition 215 (October 2021): 104735. http://dx.doi.org/10.1016/j.cognition.2021.104735.

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9

Pintelon, R., and I. Kollar. "On the Frequency Scaling in Continuous-Time Modeling." IEEE Transactions on Instrumentation and Measurement 54, no. 1 (2005): 318–21. http://dx.doi.org/10.1109/tim.2004.838916.

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10

Schoukens, J. "Modeling of continuous time systems using a discrete time representation." Automatica 26, no. 3 (1990): 579–83. http://dx.doi.org/10.1016/0005-1098(90)90029-h.

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11

Nowman, K. Ben. "REX BERGSTROM’S CONTRIBUTIONS TO CONTINUOUS TIME MACROECONOMETRIC MODELING." Econometric Theory 25, no. 4 (2009): 1087–98. http://dx.doi.org/10.1017/s0266466608090427.

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This paper reviews the contributions of Rex Bergstrom to the development of continuous time dynamic disequilibrium macroeconomic modeling since the early 1960s. The models provide an elegant integration of economic theory with analysis of steady state and stability properties. The subsequent contributions of his Ph.D. students, spawned by Bergstrom’s work over the years, is also reviewed. It was Bergstrom’s early pioneering vision 40 years ago of formulating and estimating continuous time models that underlies much of the research in that area of econometrics and finance today.

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12

Lok, Judith J. "Statistical modeling of causal effects in continuous time." Annals of Statistics 36, no. 3 (2008): 1464–507. http://dx.doi.org/10.1214/009053607000000820.

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13

Hecht, Martin, and Manuel C. Voelkle. "Continuous-time modeling in prevention research: An illustration." International Journal of Behavioral Development 45, no. 1 (2019): 19–27. http://dx.doi.org/10.1177/0165025419885026.

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The analysis of cross-lagged relationships is a popular approach in prevention research to explore the dynamics between constructs over time. However, a limitation of commonly used cross-lagged models is the requirement of equally spaced measurement occasions that prevents the usage of flexible longitudinal designs and complicates cross-study comparisons. Continuous-time modeling overcomes these limitations. In this article, we illustrate the use of continuous-time models using Bayesian and frequentist approaches to model estimation. As an empirical example, we study the dynamic interplay of p

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14

Singer, Hermann. "Nonlinear continuous time modeling approaches in panel research." Statistica Neerlandica 62, no. 1 (2007): 29–57. http://dx.doi.org/10.1111/j.1467-9574.2007.00373.x.

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15

Khodadadi, Ali, Seyed Abbas Hosseini, Erfan Tavakoli, and Hamid R. Rabiee. "Continuous-Time User Modeling in Presence of Badges." ACM Transactions on Knowledge Discovery from Data 12, no. 3 (2018): 1–30. http://dx.doi.org/10.1145/3162050.

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16

Drost, Feike C., and Bas J. M. Werker. "Closing the GARCH gap: Continuous time GARCH modeling." Journal of Econometrics 74, no. 1 (1996): 31–57. http://dx.doi.org/10.1016/0304-4076(95)01750-x.

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17

Bhandari, Nidhi, and Derrick Rollins. "Continuous-time Hammerstein nonlinear modeling applied to distillation." AIChE Journal 50, no. 2 (2004): 530–33. http://dx.doi.org/10.1002/aic.10047.

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18

Bhandari, Nidhi, and Derrick K. Rollins. "Continuous-Time Multiple-Input, Multiple-Output Wiener Modeling Method." Industrial & Engineering Chemistry Research 42, no. 22 (2003): 5583–95. http://dx.doi.org/10.1021/ie020955l.

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19

Weron, R., B. Kozłowska, and J. Nowicka-Zagrajek. "Modeling electricity loads in California: a continuous-time approach." Physica A: Statistical Mechanics and its Applications 299, no. 1-2 (2001): 344–50. http://dx.doi.org/10.1016/s0378-4371(01)00315-6.

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20

Arbia, Giuseppe, and Jean H. P. Paelinck. "Spatial Econometric Modeling of Regional Convergence in Continuous Time." International Regional Science Review 26, no. 3 (2003): 342–62. http://dx.doi.org/10.1177/0160017603255974.

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21

Ahmed, S. Ejaz. "Continuous Time Modeling in the Behavioural and Related Sciences." Technometrics 61, no. 4 (2019): 567–68. http://dx.doi.org/10.1080/00401706.2019.1629751.

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22

Söderström, Torsten, Erik Larsson, Kaushik Mahata, and Magnus Mossberg. "USING CONTINUOUS-TIME MODELING FOR ERRORS-IN-VARIABLES IDENTIFICATION." IFAC Proceedings Volumes 39, no. 1 (2006): 428–33. http://dx.doi.org/10.3182/20060329-3-au-2901.00064.

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23

Sencar, Jure, Nikolaus Hammerschmidt, and Alois Jungbauer. "Modeling the Residence Time Distribution of Integrated Continuous Bioprocesses." Biotechnology Journal 15, no. 8 (2020): 2000008. http://dx.doi.org/10.1002/biot.202000008.

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24

Velasquez, Juan David, and Carlos Jaime Franco. "Continuous Time Series Models for Modeling Daily Electricity Prices." IEEE Latin America Transactions 14, no. 8 (2016): 3630–35. http://dx.doi.org/10.1109/tla.2016.7786343.

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25

Mahata, Kaushik, and Minyue Fu. "Modeling continuous-time processes via input-to-state filters." Automatica 42, no. 7 (2006): 1073–84. http://dx.doi.org/10.1016/j.automatica.2006.02.014.

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26

Hewitt, Joshua, Robert S. Schick, and Alan E. Gelfand. "Continuous-Time Discrete-State Modeling for Deep Whale Dives." Journal of Agricultural, Biological and Environmental Statistics 26, no. 2 (2021): 180–99. http://dx.doi.org/10.1007/s13253-020-00422-2.

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27

Boer, Katalin, Uzay Kaymak, and Jaap Spiering. "FROM DISCRETE-TIME MODELS TO CONTINUOUS-TIME, ASYNCHRONOUS MODELING OF FINANCIAL MARKETS." Computational Intelligence 23, no. 2 (2007): 142–61. http://dx.doi.org/10.1111/j.1467-8640.2007.00302.x.

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28

KOHATSU-HIGA, ARTURO, and SALVADOR ORTIZ-LATORRE. "MODELING OF FINANCIAL MARKETS WITH INSIDE INFORMATION IN CONTINUOUS TIME." Stochastics and Dynamics 11, no. 02n03 (2011): 415–38. http://dx.doi.org/10.1142/s0219493711003371.

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We give a survey about the different approaches to model financial markets with inside information in continuous time. In particular, we consider the Karatzas–Pikovsky, Kyle–Back and the weak Kyle–Back approach. These three types of modeling are based on the enlargement of filtration problem, which we explain with some examples and use it for these three modeling approaches.

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29

Harker, Shane D., Mercedes T. Hernandez, and Gene Lambird. "Mechanical Modeling and Continuous Process Improvement." International Symposium on Microelectronics 2019, no. 1 (2019): 000188–92. http://dx.doi.org/10.4071/2380-4505-2019.1.000188.

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Abstract Short design cycles for integrated circuits and packages drive the need for efficient problem solving and rapid results. Improved mechanical modeling software and increased computing power have taken these computation-heavy tools and made them versatile enough to support main-stream, real-time production needs. The utility of these tools has been significantly improved by simplified work flows to create detailed geometries and complex assemblies, improved mesh generation algorithms, and solve time reduction. Mechanical modeling software has a wide range of application which traditiona

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30

Mena, Ramsés H., Matteo Ruggiero, and Stephen G. Walker. "Geometric stick-breaking processes for continuous-time Bayesian nonparametric modeling." Journal of Statistical Planning and Inference 141, no. 9 (2011): 3217–30. http://dx.doi.org/10.1016/j.jspi.2011.04.008.

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31

Burini, Diletta, Elena De Angelis, and Miroslaw Lachowicz. "A Continuous–Time Markov Chain Modeling Cancer–Immune System Interactions." Communications in Applied and Industrial Mathematics 9, no. 2 (2018): 106–18. http://dx.doi.org/10.2478/caim-2018-0018.

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Abstract In the present paper we propose two mathematical models describing, respectively at the microscopic level and at the mesoscopic level, a system of interacting tumor cells and cells of the immune system. The microscopic model is in terms of a Markov chain defined by the generator, the mesoscopic model is developed in the framework of the kinetic theory of active particles. The main result is to prove the transition from the microscopic to mesoscopic level of description.

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32

Kvam, Peter D. "Modeling accuracy, response time, and bias in continuous orientation judgments." Journal of Experimental Psychology: Human Perception and Performance 45, no. 3 (2019): 301–18. http://dx.doi.org/10.1037/xhp0000606.

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33

Kowalczuk, Zdzislaw, and Janusz Kozlowski. "Differential and integral modeling in identification of continuous-time plants." IFAC Proceedings Volumes 32, no. 2 (1999): 3974–79. http://dx.doi.org/10.1016/s1474-6670(17)56678-1.

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34

Aoun, Mohamed, Rachid Malti, Francois Levron, and Alain Oustaloup. "Orthonormal basis functions for modeling continuous-time fractional systems 1." IFAC Proceedings Volumes 36, no. 16 (2003): 1333–38. http://dx.doi.org/10.1016/s1474-6670(17)34945-5.

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35

Oud, Johan H. L., and Hermann Singer. "Continuous time modeling of panel data: SEM versus filter techniques." Statistica Neerlandica 62, no. 1 (2007): 4–28. http://dx.doi.org/10.1111/j.1467-9574.2007.00376.x.

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36

Mahata, Kaushik, and Minyue Fu. "MODELING CONTINUOUS-TIME STOCHASTIC PROCESSES USING INPUT-TO-STATE FILTERS." IFAC Proceedings Volumes 38, no. 1 (2005): 101–6. http://dx.doi.org/10.3182/20050703-6-cz-1902.00017.

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37

Boudali, H., and J. B. Dugan. "A Continuous-Time Bayesian Network Reliability Modeling, and Analysis Framework." IEEE Transactions on Reliability 55, no. 1 (2006): 86–97. http://dx.doi.org/10.1109/tr.2005.859228.

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38

Filev, Dimitar P., and Ilya Kolmanovsky. "Generalized Markov Models for Real-Time Modeling of Continuous Systems." IEEE Transactions on Fuzzy Systems 22, no. 4 (2014): 983–98. http://dx.doi.org/10.1109/tfuzz.2013.2279535.

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39

Kawai, Reiichiro. "Continuous-time modeling of random searches: statistical properties and inference." Journal of Physics A: Mathematical and Theoretical 45, no. 23 (2012): 235004. http://dx.doi.org/10.1088/1751-8113/45/23/235004.

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40

Tahk, Alexander M. "A Continuous-Time, Latent-Variable Model of Time Series Data." Political Analysis 23, no. 2 (2015): 278–98. http://dx.doi.org/10.1093/pan/mpu020.

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Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over

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41

Lin, Chieh, and Ziv Bar-Joseph. "Continuous-state HMMs for modeling time-series single-cell RNA-Seq data." Bioinformatics 35, no. 22 (2019): 4707–15. http://dx.doi.org/10.1093/bioinformatics/btz296.

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Abstract Motivation Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy. Results We developed a new method based on continuous-state HMMs (CSHMMs) for representing

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42

AGLIARI, ELENA, OLIVER MÜLKEN, and ALEXANDER BLUMEN. "CONTINUOUS-TIME QUANTUM WALKS AND TRAPPING." International Journal of Bifurcation and Chaos 20, no. 02 (2010): 271–79. http://dx.doi.org/10.1142/s0218127410025715.

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Recent findings suggest that processes such as the excitonic energy transfer through the photosynthetic antenna display quantal features, aspects known from the dynamics of charge carriers along polymer backbones. Hence, in modeling energy transfer one has to leave the classical, master-equation-type formalism and advance towards an increasingly quantum-mechanical picture, while still retaining a local description of the complex network of molecules involved in the transport, say through a tight-binding approach. Interestingly, the continuous time random walk (CTRW) picture, widely employed in

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43

Proß, Sabrina, and Bernhard Bachmann. "An Advanced Environment for Hybrid Modeling of Biological Systems Based on Modelica." Journal of Integrative Bioinformatics 8, no. 1 (2011): 1–34. http://dx.doi.org/10.1515/jib-2011-152.

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Summary Biological systems are often very complex so that an appropriate formalism is needed for modeling their behavior. Hybrid Petri Nets, consisting of time-discrete Petri Net elements as well as continuous ones, have proven to be ideal for this task. Therefore, a new Petri Net library was implemented based on the object-oriented modeling language Modelica which allows the modeling of discrete, stochastic and continuous Petri Net elements by differential, algebraic and discrete equations. An appropriate Modelica-tool performs the hybrid simulation with discrete events and the solution of co

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44

Werter, Noud P. M., Roeland De Breuker, and Mostafa M. Abdalla. "Continuous-Time State-Space Unsteady Aerodynamic Modeling for Efficient Loads Analysis." AIAA Journal 56, no. 3 (2018): 905–16. http://dx.doi.org/10.2514/1.j056068.

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45

Bennett, Trevor, and Hanspeter Schaub. "Continuous-Time Modeling and Control Using Nonsingular Linearized Relative-Orbit Elements." Journal of Guidance, Control, and Dynamics 39, no. 12 (2016): 2605–14. http://dx.doi.org/10.2514/1.g000366.

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46

Tucker, Gregory E., Daniel E. J. Hobley, Eric Hutton, et al. "CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab." Geoscientific Model Development 9, no. 2 (2016): 823–39. http://dx.doi.org/10.5194/gmd-9-823-2016.

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Abstract. CellLab-CTS 2015 is a Python-language software library for creating two-dimensional, continuous-time stochastic (CTS) cellular automaton models. The model domain consists of a set of grid nodes, with each node assigned an integer state code that represents its condition or composition. Adjacent pairs of nodes may undergo transitions to different states, according to a user-defined average transition rate. A model is created by writing a Python code that defines the possible states, the transitions, and the rates of those transitions. The code instantiates, initializes, and runs one o

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47

Flemming, A., and J. Adamy. "Modeling solid oxide fuel cells using continuous-time recurrent fuzzy systems." Engineering Applications of Artificial Intelligence 21, no. 8 (2008): 1289–300. http://dx.doi.org/10.1016/j.engappai.2008.02.006.

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48

Rollins, Derrick K., Nidhi Bhandari, Ashraf M. Bassily, Gerald M. Colver, and Swee-Teng Chin. "A Continuous-Time Nonlinear Dynamic Predictive Modeling Method for Hammerstein Processes." Industrial & Engineering Chemistry Research 42, no. 4 (2003): 860–72. http://dx.doi.org/10.1021/ie020169g.

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49

Yun-Bo Zhao, Guo-Ping Liu, and D. Rees. "Modeling and Stabilization of Continuous-Time Packet-Based Networked Control Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 6 (2009): 1646–52. http://dx.doi.org/10.1109/tsmcb.2009.2027714.

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50

Bok, Jin-Kwang, and Sunwon Park. "Continuous-Time Modeling for Short-Term Scheduling of Multipurpose Pipeless Plants." Industrial & Engineering Chemistry Research 37, no. 9 (1998): 3652–59. http://dx.doi.org/10.1021/ie9800703.

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