Sy-Miin Chow, Ph.D.

Institution:
University of Virginia, Charlottesville, VA
Area:
lifespan development
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"Sy-Miin Chow"
Mean distance: 18.59 (cluster 13)
 
SNBCP

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John Nesselroade grad student 2004 UVA
 (Fitting linear and nonlinear dynamic models using different Kalman filter approaches.)
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Publications

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Ou L, Hunter MD, Lu Z, et al. (2023) Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter. The British Journal of Mathematical and Statistical Psychology
Chen M, Chow SM, Oravecz Z, et al. (2023) Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach. Multivariate Behavioral Research. 1-25
Fredman SJ, Fischer MS, Baucom DH, et al. (2022) PTSD Symptom Cluster Severity Predicts Momentary Emotion Dynamics During Couple Conversations. Behavior Therapy. 54: 330-345
Li Y, Wood J, Ji L, et al. (2021) Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus. Structural Equation Modeling : a Multidisciplinary Journal. 29: 452-475
Cole PM, Lougheed JP, Chow SM, et al. (2020) Development of Emotion Regulation Dynamics Across Early Childhood: a Multiple Time-Scale Approach. Affective Science. 1: 28-41
Park JJ, Chow SM, Fisher ZF, et al. (2020) Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models. European Journal of Psychological Assessment : Official Organ of the European Association of Psychological Assessment. 36: 1009-1023
Fisher ZF, Chow SM, Molenaar PCM, et al. (2020) A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters. Multivariate Behavioral Research. 1-19
Bastiaansen JA, Kunkels YK, Blaauw FJ, et al. (2020) Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology. Journal of Psychosomatic Research. 137: 110211
Ji L, Chen M, Oravecz Z, et al. (2020) A Bayesian Vector Autoregressive Model with Nonignorable Missingness in Dependent Variables and Covariates: Development, Evaluation, and Application to Family Processes. Structural Equation Modeling : a Multidisciplinary Journal. 27: 442-467
Li Y, Ji L, Oravecz Z, et al. (2019) dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. World Academy of Science, Engineering and Technology. 13: 302-311
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