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Статті в журналах з теми "Time-Varying Effects"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Bandi, Federico M., and Roberto Renò. "Time-varying leverage effects." Journal of Econometrics 169, no. 1 (July 2012): 94–113. http://dx.doi.org/10.1016/j.jeconom.2012.01.010.

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2

Tanaka, Shiro, Yutaka Matsuyama, Masataka Shiraki, and Yasuo Ohashi. "Estimating the Effects of Time-Varying Treatments." Epidemiology 18, no. 5 (September 2007): 529–36. http://dx.doi.org/10.1097/ede.0b013e3181271ae2.

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3

LEONOV, G. A., and N. V. KUZNETSOV. "TIME-VARYING LINEARIZATION AND THE PERRON EFFECTS." International Journal of Bifurcation and Chaos 17, no. 04 (April 2007): 1079–107. http://dx.doi.org/10.1142/s0218127407017732.

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4

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 (August 2007): 393–434. http://dx.doi.org/10.1111/j.1467-9531.2007.00185.x.

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Анотація:

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 are time-varying. In this longitudinal setting, the research interest centers not on only two potential outcomes, but on a whole matrix of potential outcomes, requiring a complicated conceptualization of many potential counterfactuals. Motivated by sociological applications, we develop a simplification scheme—a weighted composite causal effect that allows identification and estimation of effects with a number of possible solutions. Our approach is illustrated via an analysis of the effects of disability on subsequent employment status using panel data from the Wisconsin Longitudinal Study.

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5

Dekker, Friedo W., Renée de Mutsert, Paul C. van Dijk, Carmine Zoccali, and Kitty J. Jager. "Survival analysis: time-dependent effects and time-varying risk factors." Kidney International 74, no. 8 (October 2008): 994–97. http://dx.doi.org/10.1038/ki.2008.328.

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6

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

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Анотація:

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 order to enable a correct interpretation of time-varying effects in this context, researchers should visualize their results with survivor functions. I outline how survivor functions are calculated for models with time-varying effects and demonstrate the need for such a nuanced interpretation using the prominent finding of a time-varying effect of mediation on interstate conflict. The reanalysis of the data using the proposed visualization methods indicates that the conclusions of earlier mediation research are misleading. The example highlights how survivor functions are an essential tool to clarify the ambiguity inherent in time-varying coefficients in event history models.

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7

McShane, Michael, and Trung Nguyen. "Time-varying effects of cyberattacks on firm value." Geneva Papers on Risk and Insurance - Issues and Practice 45, no. 4 (May 28, 2020): 580–615. http://dx.doi.org/10.1057/s41288-020-00170-x.

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8

Balli, Hatice Ozer, Faruk Balli, and Rosmy Jean Louis. "Time-Varying Spillover Effects on Sectoral Equity Returns." International Review of Finance 13, no. 1 (November 8, 2011): 67–91. http://dx.doi.org/10.1111/j.1468-2443.2011.01143.x.

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9

Benchimol, Jonathan, and Irfan Qureshi. "Time-varying money demand and real balance effects." Economic Modelling 87 (May 2020): 197–211. http://dx.doi.org/10.1016/j.econmod.2019.07.020.

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10

Sauerbrei, Willi, Patrick Royston, and Norbert Holländer. "S41.3: Modelling time-varying effects in survival data." Biometrical Journal 46, S1 (March 2004): 89. http://dx.doi.org/10.1002/bimj.200490296.

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11

Schneeweiss, Sebastian. "Methods in pharmacoepidemiology: time-varying drug effects revisited." Pharmacoepidemiology and Drug Safety 15, no. 2 (2006): 93–94. http://dx.doi.org/10.1002/pds.1208.

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12

Tutz, G., and H. Binder. "Flexible modelling of discrete failure time including time-varying smooth effects." Statistics in Medicine 23, no. 15 (July 19, 2004): 2445–61. http://dx.doi.org/10.1002/sim.1824.

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13

Wodtke, Geoffrey T. "Regression-based Adjustment for Time-varying Confounders." Sociological Methods & Research 49, no. 4 (May 6, 2018): 906–46. http://dx.doi.org/10.1177/0049124118769087.

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Анотація:

Social scientists are often interested in estimating the marginal effects of a time-varying treatment on an end-of-study continuous outcome. With observational data, estimating these effects is complicated by the presence of time-varying confounders affected by prior treatments, which may lead to bias in conventional regression and matching estimators. In this situation, inverse-probability-of-treatment-weighted (IPTW) estimation of a marginal structural model remains unbiased if treatment assignment is sequentially ignorable and the conditional probability of treatment is correctly modeled, but this method is not without limitations. In particular, it is difficult to use with continuous treatments, and it is relatively inefficient. This article explores using an alternative regression-based method—regression-with-residuals (RWR) estimation of a constrained structural nested mean model—that may overcome some of these limitations in practice. It is unbiased for the marginal effects of a time-varying treatment if treatment assignment is sequentially ignorable, the treatment effects of interest are invariant across levels of the confounders, and a model for the conditional mean of the outcome is correctly specified. The performance of RWR estimation relative to IPTW estimation is evaluated with a series of simulation experiments and with an empirical example based on longitudinal data from the Panel Study of Income Dynamics. Results indicate that it may outperform IPTW estimation in certain situations.

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14

Chen, Qian-xin, Yuan-zhong Yang, Zhuo-zhi Liang, Jia-li Chen, Yue-lin Li, Zi-yi Huang, Zi-jin Weng, et al. "Time-varying effects of FOXA1 on breast cancer prognosis." Breast Cancer Research and Treatment 187, no. 3 (February 18, 2021): 867–75. http://dx.doi.org/10.1007/s10549-021-06125-7.

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15

Lee, Jong-Ha, and Jin-Young Hwang. "Time-Varying Effects of Export on Employment Since 1990." Korea International Trade Research Institute 16, no. 4 (August 31, 2020): 245–60. http://dx.doi.org/10.16980/jitc.16.4.202008.245.

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16

Ingolfsson, Armann, Md Amanul Haque, and Alex Umnikov. "Accounting for time-varying queueing effects in workforce scheduling." European Journal of Operational Research 139, no. 3 (June 2002): 585–97. http://dx.doi.org/10.1016/s0377-2217(01)00169-2.

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17

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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18

Ahn, Seung C., Young H. Lee, and Peter Schmidt. "Stochastic frontier models with multiple time-varying individual effects." Journal of Productivity Analysis 27, no. 1 (December 20, 2006): 1–12. http://dx.doi.org/10.1007/s11123-006-0020-8.

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19

Özlale, Ümit, and Levent Özbek. "Analyzing time-varying effects of potential output growth shocks." Economics Letters 98, no. 3 (March 2008): 294–300. http://dx.doi.org/10.1016/j.econlet.2007.05.006.

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20

Dunson, David B., Patricia Chulada, and Samuel J. Arbes. "Bayesian Modeling of Time-Varying and Waning Exposure Effects." Biometrics 59, no. 1 (March 2003): 83–91. http://dx.doi.org/10.1111/1541-0420.00010.

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21

GLASER, DARRELL J. "TIME-VARYING EFFECTS OF HUMAN CAPITAL ON MILITARY RETENTION." Contemporary Economic Policy 29, no. 2 (April 2011): 231–49. http://dx.doi.org/10.1111/j.1465-7287.2010.00220.x.

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22

Jatoi, Ismail, Hanna Bandos, Jong-Hyeon Jeong, William F. Anderson, Edward H. Romond, Eleftherios P. Mamounas, and Norman Wolmark. "Time-Varying Effects of Breast Cancer Adjuvant Systemic Therapy." Journal of the National Cancer Institute 108, no. 1 (October 30, 2015): djv304. http://dx.doi.org/10.1093/jnci/djv304.

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23

Schuetz, P. W., J. C. Barbenel, and J. P. Paul. "Effects of time-varying magnetic fields on fibroblast growth." Clinical Physics and Physiological Measurement 6, no. 2 (May 1985): 155–60. http://dx.doi.org/10.1088/0143-0815/6/2/007.

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24

Ahn, Seung C., Young H. Lee, and Peter Schmidt. "Panel data models with multiple time-varying individual effects." Journal of Econometrics 174, no. 1 (May 2013): 1–14. http://dx.doi.org/10.1016/j.jeconom.2012.12.002.

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25

Zhao, Shouwei, Jitao Sun, and Li Liu. "Finite-time stability of linear time-varying singular systems with impulsive effects." International Journal of Control 81, no. 11 (November 2008): 1824–29. http://dx.doi.org/10.1080/00207170801898893.

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26

Grant, B. J., J. M. Fitzpatrick, and B. B. Lieber. "Time-varying pulmonary arterial compliance." Journal of Applied Physiology 70, no. 2 (February 1, 1991): 575–83. http://dx.doi.org/10.1152/jappl.1991.70.2.575.

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Анотація:

We tested the hypothesis that pulmonary arterial compliance (Ca) varies during the ventilatory cycle. Pressure and flow in the main pulmonary artery were measured in open-chest dogs under chloralose anesthesia (n = 12) with a positive-pressure volume-cycled ventilator. Input impedance was calculated from the pressure and flow waves of heart cycles obtained immediately after the start of inspiration (SI) and immediately after the start of expiration (SE). A lumped parameter model was used to calculate Ca from the input impedance spectrum of the main pulmonary artery. Three levels of positive end-expiratory pressure (PEEP) were used before and after meclofenamate (n = 6) or vagotomy (n = 6). Ca was significantly greater at SE than at SI at each level of PEEP. PEEP increased Ca at SE but not at SI. None of these changes was altered by meclofenamate or vagotomy, suggesting that these differences of Ca were due to passive mechanical effects rather than an active neurohumoral mechanisms. We conclude that Ca is time varying during the ventilatory cycle because it is altered by the dynamic increase of lung volume between SI and SE, but not with the quasi-static increase of lung volume induced by raising the level of PEEP. These changes of Ca were unaffected by vagal feedback or inhibition of cyclooxygenase. We suggest that the increased Ca just after the start of expiration may result from dynamic shifts of blood volume from the extra-alveolar to the alveolar vessels.

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27

Zuk, Nathaniel, and Bertrand Delgutte. "Neural coding of time-varying interaural time differences and time-varying amplitude in the inferior colliculus." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 544–63. http://dx.doi.org/10.1152/jn.00797.2016.

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Binaural cues occurring in natural environments are frequently time varying, either from the motion of a sound source or through interactions between the cues produced by multiple sources. Yet, a broad understanding of how the auditory system processes dynamic binaural cues is still lacking. In the current study, we directly compared neural responses in the inferior colliculus (IC) of unanesthetized rabbits to broadband noise with time-varying interaural time differences (ITD) with responses to noise with sinusoidal amplitude modulation (SAM) over a wide range of modulation frequencies. On the basis of prior research, we hypothesized that the IC, one of the first stages to exhibit tuning of firing rate to modulation frequency, might use a common mechanism to encode time-varying information in general. Instead, we found weaker temporal coding for dynamic ITD compared with amplitude modulation and stronger effects of adaptation for amplitude modulation. The differences in temporal coding of dynamic ITD compared with SAM at the single-neuron level could be a neural correlate of “binaural sluggishness,” the inability to perceive fluctuations in time-varying binaural cues at high modulation frequencies, for which a physiological explanation has so far remained elusive. At ITD-variation frequencies of 64 Hz and above, where a temporal code was less effective, noise with a dynamic ITD could still be distinguished from noise with a constant ITD through differences in average firing rate in many neurons, suggesting a frequency-dependent tradeoff between rate and temporal coding of time-varying binaural information. NEW & NOTEWORTHY Humans use time-varying binaural cues to parse auditory scenes comprising multiple sound sources and reverberation. However, the neural mechanisms for doing so are poorly understood. Our results demonstrate a potential neural correlate for the reduced detectability of fluctuations in time-varying binaural information at high speeds, as occurs in reverberation. The results also suggest that the neural mechanisms for processing time-varying binaural and monaural cues are largely distinct.

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28

Hong, Guanglei, and Stephen W. Raudenbush. "Causal Inference for Time-Varying Instructional Treatments." Journal of Educational and Behavioral Statistics 33, no. 3 (September 2008): 333–62. http://dx.doi.org/10.3102/1076998607307355.

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The authors propose a strategy for studying the effects of time-varying instructional treatments on repeatedly observed student achievement. This approach responds to three challenges: (a) The yearly reallocation of students to classrooms and teachers creates a complex structure of dependence among responses; (b) a child’s learning outcome under a certain treatment may depend on the treatment assignment of other children, the skill of the teacher, and the classmates and teachers encountered in the past years; and (c) time-varying confounding poses special problems of endogeneity. The authors address these challenges by modifying the stable unit treatment value assumption to identify potential outcomes and causal effects and by integrating inverse probability of treatment weighting into a four-way value-added hierarchical model with pseudolikelihood estimation. Using data from the Longitudinal Analysis of School Change and Performance, the authors apply these methods to study the impact of “intensive math instruction” in Grades 4 and 5.

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29

Sterne, Jonathan A. C., and Kate Tilling. "G-estimation of Causal Effects, Allowing for Time-varying Confounding." Stata Journal: Promoting communications on statistics and Stata 2, no. 2 (June 2002): 164–82. http://dx.doi.org/10.1177/1536867x0200200205.

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This article describes the stgest command, which implements G-estimation (as proposed by Robins) to estimate the effect of a time-varying exposure on survival time, allowing for time-varying confounders.

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30

Wodtke, Geoffrey T., and Daniel Almirall. "Estimating Moderated Causal Effects with Time-varying Treatments and Time-varying Moderators: Structural Nested Mean Models and Regression with Residuals." Sociological Methodology 47, no. 1 (April 27, 2017): 212–45. http://dx.doi.org/10.1177/0081175017701180.

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Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal settings in which both the treatment and effect moderator vary over time. Effect moderation is typically examined using covariate by treatment interactions in regression analyses, but in the longitudinal setting, this approach may be problematic because time-varying moderators of future treatment may be affected by prior treatment—for example, moderators may also be mediators—and naively conditioning on an outcome of treatment in a conventional regression model can lead to bias. This article introduces to sociology moderated intermediate causal effects and the structural nested mean model for analyzing effect moderation in the longitudinal setting. It discusses problems with conventional regression and presents a new approach to estimation (regression with residuals) that avoids these problems. The method is illustrated using longitudinal data from the Panel Study of Income Dynamics to examine whether the effects of time-varying exposures to poor neighborhoods on the risk for adolescent childbearing are moderated by time-varying family income.

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31

Chen, Kaiyi, and Jianguo Sun. "Variable selection for time-varying effects based on interval-censored failure time data." Statistics and Its Interface 15, no. 3 (2022): 303–11. http://dx.doi.org/10.4310/21-sii687.

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32

Tan, Jie, and Chuandong Li. "Finite-Time Stability of Neural Networks with Impulse Effects and Time-Varying Delay." Neural Processing Letters 46, no. 1 (November 30, 2016): 29–39. http://dx.doi.org/10.1007/s11063-016-9570-6.

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33

Winnett, Angela, and Peter Sasieni. "Iterated residuals and time-varying covariate effects in Cox regression." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65, no. 2 (April 25, 2003): 473–88. http://dx.doi.org/10.1111/1467-9868.00397.

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34

Joffe, M. "Weaker Assumptions for Estimating the Effects of Time-Varying Exposures." American Journal of Epidemiology 163, suppl_11 (June 1, 2006): S226. http://dx.doi.org/10.1093/aje/163.suppl_11.s226-a.

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35

Ben Omrane, Walid, Tanseli Savaser, Robert Welch, and Xinyao Zhou. "Time-varying effects of macroeconomic news on euro-dollar returns." North American Journal of Economics and Finance 50 (November 2019): 101001. http://dx.doi.org/10.1016/j.najef.2019.101001.

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36

Ramanathan, Suresh, and Geeta Menon. "Time-Varying Effects of Chronic Hedonic Goals on Impulsive Behavior." Journal of Marketing Research 43, no. 4 (November 2006): 628–41. http://dx.doi.org/10.1509/jmkr.43.4.628.

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37

Barnes, F. S. "The effects of time varying magnetic fields on biological materials." IEEE Transactions on Magnetics 26, no. 5 (1990): 2092–97. http://dx.doi.org/10.1109/20.104630.

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38

Orji, Okwudili C., Walter Söllner, and Leiv-J. Gelius. "Effects of time-varying sea surface in marine seismic data." GEOPHYSICS 77, no. 3 (May 1, 2012): P33—P43. http://dx.doi.org/10.1190/geo2011-0361.1.

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Анотація:

A method of imaging sea surfaces based on marine seismic measurements has recently been developed. The imaging technique is based on extrapolating decomposed wavefields obtained from dual-sensor streamers to the sea surface where an adequate imaging condition is applied. Earlier feasibility tests of the method involved only controlled data associated with frozen sea surfaces. Here, the issue of time-varying effects will be in focus. We introduced a modeling approach based on the Kirchhoff-Helmholtz integral and computed the scattered wavefield from time-varying rough sea surfaces (e.g., Pierson-Moskowitz sea surfaces). We generated data for a realistic wind speed and verify the robustness of the proposed sea surface imaging technique by taking into account possible effects of moving receivers as well as streamers with variable shape. We investigate the feasibility of estimating the surface wave velocity from the spectra of the imaged sea surfaces and finally present a successful application of the sea surface imaging technique to data from the North Sea.

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39

Şentürk, Damla, Lorien S. Dalrymple, Sandra M. Mohammed, George A. Kaysen, and Danh V. Nguyen. "Modeling time-varying effects with generalized and unsynchronized longitudinal data." Statistics in Medicine 32, no. 17 (January 18, 2013): 2971–87. http://dx.doi.org/10.1002/sim.5740.

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40

Elliott, J. P., R. L. Smith, and C. A. Block. "Time-varying magnetic fields: Effects of orientation on chondrocyte proliferation." Journal of Orthopaedic Research 6, no. 2 (March 1988): 259–64. http://dx.doi.org/10.1002/jor.1100060213.

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41

Kutlu, Levent, Kien C. Tran, and Mike G. Tsionas. "A time-varying true individual effects model with endogenous regressors." Journal of Econometrics 211, no. 2 (August 2019): 539–59. http://dx.doi.org/10.1016/j.jeconom.2019.01.014.

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42

Hutzell, William T., Christopher P. McKay, and Owen B. Toon. "Effects of Time-Varying Haze Production on Titan's Geometric Albedo." Icarus 105, no. 1 (September 1993): 162–74. http://dx.doi.org/10.1006/icar.1993.1116.

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43

Al-Kindi, Sadeer, Issam Motairek, Scott Janus, Salil Deo, Mahboob Rahman, Ian J. Neeland, and Sanjay Rajagopalan. "Time-Varying Cardiovascular Effects of Finerenone in Diabetic Kidney Disease." Journal of the American College of Cardiology 80, no. 19 (November 2022): 1855–56. http://dx.doi.org/10.1016/j.jacc.2022.08.791.

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44

Silva, Luciano A., Eric M. Austin, and Daniel J. Inman. "Time-Varying Controller for Temperature-Dependent Viscoelasticity." Journal of Vibration and Acoustics 127, no. 3 (June 1, 2005): 215–22. http://dx.doi.org/10.1115/1.1897740.

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Анотація:

There are internal-variable techniques that account for the frequency-dependent behavior of viscoelastic materials, but the temperature dependence of these materials has received much less attention. Two methods for designing controllers robust to temperature disturbances are given: (1) Modal reference adaptive control and (2) time-varying pole placement control. Examples that demonstrate the strengths and weaknesses of each are given. The results show that it is possible to achieve vibration reduction while simultaneously rejecting the effects of a temperature disturbance. This work shows that both the frequency and temperature dependence of viscoelastic materials can be modeled with internal variables.

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45

GRÜNE, LARS. "ROBUST ASYMPTOTIC CONTROLLABILITY UNDER TIME-VARYING PERTURBATIONS." Stochastics and Dynamics 04, no. 03 (September 2004): 297–316. http://dx.doi.org/10.1142/s0219493704001085.

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Анотація:

We investigate the effect of time-varying perturbations on the dynamical behavior of nonlinear control systems. More specifically, we study the effect of such perturbations on the controlled equivalent of asymptotically stable sets, i.e. asymptotically controllable sets. In the first part of this paper we illustrate by a simple example how different types of perturbations affect this dynamical behavior and use concepts from dynamical game theory in order to identify classes of perturbations which allow to model the effects of numerical discretization errors both in time and space. In the second part we introduce appropriate robustness properties and prove that these are inherent properties for asymptotically controllable sets under these classes of perturbations.

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46

Wu, Colin O., Xin Tian, and Heejung Bang. "A varying‐coefficient model for the evaluation of time‐varying concomitant intervention effects in longitudinal studies." Statistics in Medicine 27, no. 16 (July 20, 2008): 3042–56. http://dx.doi.org/10.1002/sim.3262.

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47

Rafi, R. S., S. Fardad, and A. Salandrino. "Intermodal excitation in time-varying plasmonic structures." Applied Physics Letters 122, no. 4 (January 23, 2023): 041701. http://dx.doi.org/10.1063/5.0132243.

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Анотація:

Space and time-varying electromagnetic structures give access to regimes of operation and effects that ordinarily do not occur in their time-invariant counterparts due to modal orthogonality constraints. Here, we present the theory of intermodal energy transfer in time-varying plasmonic structures. After identifying a suitable physical mechanism of permittivity modulation, we introduce an appropriate time-domain formalism to study the evolution of the dielectric polarization density in the system. Using a perturbative approach, we obtain closed-form solutions describing the intermodal energy transfer between a directly excited dipolar mode and a higher order subradiant mode. We further show that the modal amplitudes reach a steady state and determine the optimal modulation conditions that maximize the amplitude of the high-order mode. Finally, we identify a coherent control strategy to enhance the conversion efficiency to higher order modes.

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48

Liu, Yang, and Shouwei Zhao. "Controllability analysis of linear time-varying systems with multiple time delays and impulsive effects." Nonlinear Analysis: Real World Applications 13, no. 2 (April 2012): 558–68. http://dx.doi.org/10.1016/j.nonrwa.2011.07.038.

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49

Zochowski, Thomas, Elizabeth Johnson, and Gordon Sleivert. "Effects of Varying Post Warm-Up Recovery Time on 200m Time Trial Swim Performance." Medicine & Science in Sports & Exercise 38, Supplement (May 2006): S231. http://dx.doi.org/10.1249/00005768-200605001-01894.

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50

Galai, Noya, Elisheva Simchen, Dalit Braun, Micha Mandel, and Yana Zitser-Gurevich. "Evaluating inter-hospital variability in mortality rates over time, allowing for time-varying effects." Statistics in Medicine 21, no. 1 (2001): 21–33. http://dx.doi.org/10.1002/sim.936.

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