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Journal articles on the topic 'Time-varying data analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Colominas, Marcelo A., Mohamad El Sayed Hussein Jomaa, Nisrine Jrad, Anne Humeau-Heurtier, and Patrick Van Bogaert. "Time-Varying Time–Frequency Complexity Measures for Epileptic EEG Data Analysis." IEEE Transactions on Biomedical Engineering 65, no. 8 (2018): 1681–88. http://dx.doi.org/10.1109/tbme.2017.2761982.

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2

Murphy, Susan A., Gillian R. Bentley, and Mary Ann O'Hanesian. "An analysis for menstrual data with time-varying covariates." Statistics in Medicine 14, no. 17 (1995): 1843–57. http://dx.doi.org/10.1002/sim.4780141702.

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3

Jeong, Seonghyun, Minjae Park, and Taeyoung Park. "Analysis of binary longitudinal data with time-varying effects." Computational Statistics & Data Analysis 112 (August 2017): 145–53. http://dx.doi.org/10.1016/j.csda.2017.03.007.

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4

Kim, Yang-Jin. "Analysis of Recurrent Gap Time Data with a Binary Time-Varying Covariate." Communications for Statistical Applications and Methods 21, no. 5 (2014): 387–93. http://dx.doi.org/10.5351/csam.2014.21.5.387.

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5

JACOBS, CHRISTOPHER R., CLARE E. YELLOWLEY, DREW V. NELSON, and HENRY J. DONAHUE. "Analysis of Time-Varying Biological Data Using Rainflow Cycle Counting." Computer Methods in Biomechanics and Biomedical Engineering 3, no. 1 (2000): 31–40. http://dx.doi.org/10.1080/10255840008915252.

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6

Sutton, Eric, and Helen Na. "Time-varying reconstruction of the ionosphere. 2. Data source analysis." International Journal of Imaging Systems and Technology 9, no. 6 (1998): 491–99. http://dx.doi.org/10.1002/(sici)1098-1098(1998)9:6<491::aid-ima11>3.0.co;2-0.

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7

Li, Shanshan, Yifei Sun, Chiung-Yu Huang, Dean A. Follmann, and Richard Krause. "Recurrent event data analysis with intermittently observed time-varying covariates." Statistics in Medicine 35, no. 18 (2016): 3049–65. http://dx.doi.org/10.1002/sim.6901.

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8

Zivanovic, Miroslav. "Time-Varying Multicomponent Signal Modeling for Analysis of Surface EMG Data." IEEE Signal Processing Letters 21, no. 6 (2014): 692–96. http://dx.doi.org/10.1109/lsp.2014.2313880.

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9

Wong, May C. M., K. F. Lam, and Edward C. M. Lo. "Analysis of multilevel grouped survival data with time-varying regression coefficients." Statistics in Medicine 30, no. 3 (2010): 250–59. http://dx.doi.org/10.1002/sim.4094.

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10

Liu, Guochang, Sergey Fomel, and Xiaohong Chen. "Time-frequency analysis of seismic data using local attributes." GEOPHYSICS 76, no. 6 (2011): P23—P34. http://dx.doi.org/10.1190/geo2010-0185.1.

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Time-frequency analysis is an important technology in seismic data processing and interpretation. To localize frequency content in time, we have developed a novel method for computing a time-frequency map for nonstationary signals using an iterative inversion framework. We calculated time-varying Fourier coefficients by solving a least-squares problem that uses regularized nonstationary regression. We defined the time-frequency map as the norm of time-varying coefficients. Time-varying average frequency of the seismic data can also be estimated from the time-frequency map calculated by our met

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11

RAGOZINI, GIANCARLO, DOMENICO DE STEFANO, and MARIA ROSARIA D'ESPOSITO. "Multiple factor analysis for time-varying two-mode networks." Network Science 3, no. 1 (2015): 18–36. http://dx.doi.org/10.1017/nws.2015.5.

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AbstractMost social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode network

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12

Nielsen, Rasmus Oestergaard, Michael Lejbach Bertelsen, Daniel Ramskov, et al. "Time-to-event analysis for sports injury research part 1: time-varying exposures." British Journal of Sports Medicine 53, no. 1 (2018): 61–68. http://dx.doi.org/10.1136/bjsports-2018-099408.

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Background‘How much change in training load is too much before injury is sustained, among different athletes?’ is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology.AimTo discuss advanced statistical methods suitable for the complex analysis of time-

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13

Sun, Liuquan, Liang Zhu, and Jianguo Sun. "Regression analysis of multivariate recurrent event data with time-varying covariate effects." Journal of Multivariate Analysis 100, no. 10 (2009): 2214–23. http://dx.doi.org/10.1016/j.jmva.2009.08.001.

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14

Xiao, Feng, Yang Shi, and Wei Ren. "Robustness Analysis of Asynchronous Sampled-Data Multiagent Networks With Time-Varying Delays." IEEE Transactions on Automatic Control 63, no. 7 (2018): 2145–52. http://dx.doi.org/10.1109/tac.2017.2756860.

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15

Skup, Martha, Hongtu Zhu, and Heping Zhang. "Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time-Varying Covariates." Biometrics 68, no. 4 (2012): 1083–92. http://dx.doi.org/10.1111/j.1541-0420.2012.01767.x.

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16

linusokafor, Uchenwa, O. Oladejo, DT Chinyo, and CO Uwa. "VERSATILITY OF TIME-VARYING AMPLITUDE METHOD IN HARMONIC ANALYSIS OF DISCRETE DATA." International Journal of Advanced Research 7, no. 1 (2019): 1037–40. http://dx.doi.org/10.21474/ijar01/8419.

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17

Dutta, Soumya, and Han-Wei Shen. "Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis." IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 837–46. http://dx.doi.org/10.1109/tvcg.2015.2467436.

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18

Zhao, Haiyan, Fred Huffer, and Xu-Feng Niu. "Time-varying coefficient models with ARMA–GARCH structures for longitudinal data analysis." Journal of Applied Statistics 42, no. 2 (2014): 309–26. http://dx.doi.org/10.1080/02664763.2014.949638.

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19

Luo, Haocheng, and Zechun Hu. "Stability analysis of sampled-data control systems with multiple time-varying delays." Journal of the Franklin Institute 357, no. 11 (2020): 6615–34. http://dx.doi.org/10.1016/j.jfranklin.2020.04.021.

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20

Chen, Ray-Bing, Ying Chen, and Wolfgang K. Härdle. "TVICA—Time varying independent component analysis and its application to financial data." Computational Statistics & Data Analysis 74 (June 2014): 95–109. http://dx.doi.org/10.1016/j.csda.2014.01.002.

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21

Donnelly, C. J., C. Alexander, T. C. Pataky, K. Stannage, S. Reid, and M. A. Robinson. "Vector-field statistics for the analysis of time varying clinical gait data." Clinical Biomechanics 41 (January 2017): 87–91. http://dx.doi.org/10.1016/j.clinbiomech.2016.11.008.

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22

Liu, Shu, Jinhong You, and Heng Lian. "Estimation and model identification of longitudinal data time-varying nonparametric models." Journal of Multivariate Analysis 156 (April 2017): 116–36. http://dx.doi.org/10.1016/j.jmva.2017.02.003.

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23

Chyan, Chuan-Jen, Nguyen Huu Du, and Vu Hoang Linh. "On data-dependence of exponential stability and stability radii for linear time-varying differential-algebraic systems." Journal of Differential Equations 245, no. 8 (2008): 2078–102. http://dx.doi.org/10.1016/j.jde.2008.07.016.

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24

Moyano, Iván. "Controllability of a 2D quantum particle in a time-varying disc with radial data." Journal of Mathematical Analysis and Applications 455, no. 2 (2017): 1323–50. http://dx.doi.org/10.1016/j.jmaa.2017.05.002.

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25

Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (2016): 159–80. http://dx.doi.org/10.1017/apr.2016.48.

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AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood

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26

Sakthivel, R., Chao Wang, Srimanta Santra, and B. Kaviarasan. "Non-fragile reliable sampled-data controller for nonlinear switched time-varying systems." Nonlinear Analysis: Hybrid Systems 27 (February 2018): 62–76. http://dx.doi.org/10.1016/j.nahs.2017.08.005.

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27

Nielsen, Rasmus Oestergaard, Michael Lejbach Bertelsen, Daniel Ramskov, et al. "Time-to-event analysis for sports injury research part 2: time-varying outcomes." British Journal of Sports Medicine 53, no. 1 (2018): 70–78. http://dx.doi.org/10.1136/bjsports-2018-100000.

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BackgroundTime-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain.ContentIn the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included i

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28

Zhu, Shuangyi, Yuan Ma, Xusheng Li, Jing Yang, Jingqiang Lin, and Jiwu Jing. "On the Analysis and Improvement of Min-Entropy Estimation on Time-Varying Data." IEEE Transactions on Information Forensics and Security 15 (2020): 1696–708. http://dx.doi.org/10.1109/tifs.2019.2947871.

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29

Liu, Song, Lei Peng, and Lin Lin Yuan. "Research on Deformation Analysis of Time-Varying Point Cloud." Applied Mechanics and Materials 556-562 (May 2014): 3450–55. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3450.

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For large-scale object or scene which needs high requirements of deformation detection, a comprehensive deformation analysis method is proposed based on the time-varying point cloud to perform continuous detection, to comprehensively analyze the deformation and to research its characteristics and rules. In order to improve computing efficiency, a BSP parallel algorithm based on deformation analysis of time-varying point cloud is designed according to BSP parallel computing technology, and the deformational data are handled by a HAMA computing cluster which is composed of common personal comput

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30

Brand, Jennie E., and Yu Xie. "11. Identification and Estimation of Causal Effects with Time-Varying Treatments and Time-Varying Outcomes." Sociological Methodology 37, no. 1 (2007): 393–434. http://dx.doi.org/10.1111/j.1467-9531.2007.00185.x.

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We develop an approach to identifying and estimating causal effects in longitudinal settings with time-varying treatments and time-varying outcomes. The classic potential outcome approach to causal inference generally involves two time periods: units of analysis are exposed to one of two possible values of the causal variable, treatment or control, at a given point in time, and values for an outcome are assessed some time subsequent to exposure. In this paper, we develop a potential outcome approach for longitudinal situations in which both exposure to treatment and the effects of treatment ar

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31

Krystal, A. "New methods of time series analysis of non-stationary EEG data: eigenstructure decompositions of time varying autoregressions." Clinical Neurophysiology 110, no. 12 (1999): 2197–206. http://dx.doi.org/10.1016/s1388-2457(99)00165-0.

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32

Trimech, Anyssa. "Time-varying dependence measures: a comparative analysis through wavelet approach." International Journal of Energy Sector Management 11, no. 2 (2017): 350–64. http://dx.doi.org/10.1108/ijesm-01-2016-0001.

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Purpose This paper aims to investigate the pattern of dependence between crude oil price and energy consumption of the most important economic sectors in the USA, over different time periods, using monthly data set from January 1986 to July 2014 and a comparative study between linear correlation versus copula correlation as a measure of dependence over the single scale and the multiscale analysis. Design/methodology/approach The proposed method is based on the multiresolution analysis which gives more extensive and detailed description of the dependence price-consumption pattern over different

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33

Jawadi, Fredj. "INTRODUCTION TO TIME-VARYING MODELING WITH MACROECONOMIC AND FINANCIAL DATA." Macroeconomic Dynamics 16, S2 (2012): 167–75. http://dx.doi.org/10.1017/s136510051100071x.

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The dynamics of macroeconomic and financial series has evolved swiftly and asymmetrically since the end of the 1970s, and their statistical properties have also changed over time, suggesting complex relationships between economic and financial variables. The transformations can be explained by considerable changes in householder's behavior, market structures, and economic systems and by the alternation of exogenous shocks and financial crises that have affected the economic cycle, with significant evidence of time variation in the major economic variables. Hence, there is a need for new econom

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34

Ramdani, Nacim, Yves Candau, Gilles Guyon, and Christophe Dalibart. "Sensitivity Analysis of Dynamic Models to Uncertainties in Inputs Data With Time-Varying Variances." Technometrics 48, no. 1 (2006): 74–87. http://dx.doi.org/10.1198/004017005000000337.

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35

Lee, Duncan, and Gavin Shaddick. "Time-Varying Coefficient Models for the Analysis of Air Pollution and Health Outcome Data." Biometrics 63, no. 4 (2007): 1253–61. http://dx.doi.org/10.1111/j.1541-0420.2007.00776.x.

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36

Samidurai, R., and R. Sriraman. "Non-fragile sampled-data stabilization analysis for linear systems with probabilistic time-varying delays." Journal of the Franklin Institute 356, no. 8 (2019): 4335–57. http://dx.doi.org/10.1016/j.jfranklin.2018.11.046.

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37

Zheng, Xueying, Lan Xue, and Annie Qu. "Time-varying correlation structure estimation and local-feature detection for spatio-temporal data." Journal of Multivariate Analysis 168 (November 2018): 221–39. http://dx.doi.org/10.1016/j.jmva.2018.07.012.

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38

Vrahatis, Aristidis G., Konstantina Dimitrakopoulou, Panos Balomenos, Athanasios K. Tsakalidis, and Anastasios Bezerianos. "CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis." Bioinformatics 32, no. 6 (2015): 884–92. http://dx.doi.org/10.1093/bioinformatics/btv673.

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Abstract Motivation: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific ‘active parts’ of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical ‘themes’—in the form of enriched biologically relevant microRNA-mediated subpathways—that determine the functionality of signaling networks across time. Results: To address these challenges, we developed time-vaRying enriCHment integ

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39

Minjie, Zheng, Zhou Yujie, Yang Shenhua, and Li Lina. "Sampled-Data Control of Singular Systems with Time Delays." Abstract and Applied Analysis 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/569878.

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This paper is concerned with sampled-data controller design for singular systems with time delay. It is assumed that the sampling periods are arbitrarily varying but bounded. A time-dependent Lyapunov function is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Combining input delay approach with Lyapunov method, sufficient conditions are derived which guarante that the singular system is regular, impulse free, and exponentially stable. Then, the existence conditions of desired sampled-data controller can be obtained, w

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40

Cao, Ying, Suja S. Rajan, and Peng Wei. "Mendelian randomization analysis of a time-varying exposure for binary disease outcomes using functional data analysis methods." Genetic Epidemiology 40, no. 8 (2016): 744–55. http://dx.doi.org/10.1002/gepi.22013.

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41

Chi, Ronghu, Zhongsheng Hou, and Shangtai Jin. "Data-Weighting Periodic RLS Based Adaptive Control Design and Analysis without Linear Growth Condition." Journal of Applied Mathematics 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/191256.

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A new periodic recursive least-squares (PRLS) estimator is developed with data-weighting factors for a class of linear time-varying parametric systems where the uncertain parameters are periodic with a known periodicity. The periodical time-varying parameter can be regarded as a constant in the time interval of a periodicity. Then the proposed PRLS estimates the unknown time-varying parameter from period to period in batches. By using equivalent feedback principle, the feedback control law is constructed for the adaptive control. Another distinct feature of the proposed PRLS-based adaptive con

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42

D'ADDONA, STEFANO, and MATTIA CIPRIAN. "TIME VARYING SENSITIVITIES ON A GRID ARCHITECTURE." International Journal of Theoretical and Applied Finance 10, no. 02 (2007): 307–29. http://dx.doi.org/10.1142/s0219024907004214.

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We investigate the gains obtained by using GRID, an innovative web-based technology for parallel computing, in a Risk Management application. We show, by estimating a parametric Value at Risk, how GRID computing offers an opportunity to enhance the solution of computationally demanding problems with decentralized data retrieval. Furthermore, we also provide an analysis of the risk factors in the US market, by empirically testing, on the Fama and French database, a classic one factor model augmented with a time varying specification of beta.

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43

Ruhe, Constantin. "Quantifying Change Over Time: Interpreting Time-varying Effects In Duration Analyses." Political Analysis 26, no. 1 (2018): 90–111. http://dx.doi.org/10.1017/pan.2017.35.

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Duration analyses in political science often model nonproportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a useful, initial postestimation step, I argue that they are insufficient to identify the overall impact of a time-varying effect and may lead to faulty inference when a coefficient changes its sign. I show how even significant changes of a coefficient’s sign do not imply that the overall effect is reversed over time. In orde

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44

IKOMA, Norikazu. "Time Series Analysis based on Time-Varying Peak Frequencies of Power Spectrum and Application to Seismic Wave Data." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1996 (May 5, 1996): 133–38. http://dx.doi.org/10.5687/sss.1996.133.

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45

Kim, Seong-Eun, Michael K. Behr, Demba Ba, and Emery N. Brown. "State-space multitaper time-frequency analysis." Proceedings of the National Academy of Sciences 115, no. 1 (2017): E5—E14. http://dx.doi.org/10.1073/pnas.1702877115.

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Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical

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46

Moerbeek, Mirjam, and Lieke Hesen. "The Consequences of Varying Measurement Occasions in Discrete-Time Survival Analysis." Methodology 14, no. 2 (2018): 45–55. http://dx.doi.org/10.1027/1614-2241/a000145.

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Abstract. In a discrete-time survival model the occurrence of some event is measured by the end of each time interval. In practice it is not always possible to measure all subjects at the same point in time. In this study the consequences of varying measurement occasions are investigated by means of a simulation study and the analysis of data from an empirical study. The results of the simulation study suggest that the effects of varying measurement occasions are negligible, at least for the scenarios that were covered in the simulation. The empirical example shows varying measurement occasion

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47

Keuleers, Bertold, Geert Wets, Harry Timmermans, Theo Arentze, and Koen Vanhoof. "Stationary and Time-Varying Patterns in Activity Diary Panel Data: Explorative Analysis with Association Rules." Transportation Research Record: Journal of the Transportation Research Board 1807, no. 1 (2002): 9–15. http://dx.doi.org/10.3141/1807-02.

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The question of identifying temporal patterns in activity diary data has received only scant attention in the transportation literature, but interest is rapidly increasing. Most of the existing research uses well-known econometric methods to quantify change. Use of association rules to explore activity diary panel data, involving two waves, for possible stationary and time-varying patterns in activity-travel patterns is reported. The data for this analysis stem from the municipality of Voorhout in the Netherlands. Data were collected in 1997 and 1998 before and after opening of a new railway s

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48

Peng, Qifeng, Peng Xu, Hong Yuan, et al. "Analysis of Vibration Monitoring Data of Flexible Suspension Lifting Structure Based on Time-Varying Theory." Sensors 20, no. 22 (2020): 6586. http://dx.doi.org/10.3390/s20226586.

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An elevator is a typical flexible lifting machine. In order to monitor the vibration of elevator structure, the vibration characteristics of an elevator with a traction ratio of 1:1 has been tested experimentally. Sensors were arranged on the platform frame, car roof, and hoist rope to test the vibrations of the elevator in both ascending and descending conditions. The transverse, longitudinal, and coupled transverse-longitudinal vibrations were compared and analyzed. Further, the short-time Fourier transform (STFT) method was used to examine the power spectral density (PSD) of the test result

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Furukawa, Kyoji, Dale L. Preston, Munechika Misumi, and Harry M. Cullings. "Handling incomplete smoking history data in survival analysis." Statistical Methods in Medical Research 26, no. 2 (2014): 707–23. http://dx.doi.org/10.1177/0962280214556794.

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While data are unavoidably missing or incomplete in most observational studies, consequences of mishandling such incompleteness in analysis are often overlooked. When time-varying information is collected irregularly and infrequently over a long period, even precisely obtained data may implicitly involve substantial incompleteness. Motivated by an analysis to quantitatively evaluate the effects of smoking and radiation on lung cancer risks among Japanese atomic-bomb survivors, we provide a unique application of multiple imputation to incompletely observed smoking histories under the assumption

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Soriano-Vargas, Aurea, Bernd Hamann, and Maria Cristina F de Oliveira. "TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data." Information Visualization 19, no. 1 (2019): 3–23. http://dx.doi.org/10.1177/1473871619858937.

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We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualizati

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