Path and Directionality Discovery in Individual Dynamic Models: A Regularized Structural Equation Modeling Approach
Ai Ye
Department of Psychology and Neuroscience
The University of North Carolina at Chapel Hill, U.S.A.
Location: ZOOM
Meeting ID: 926 8545 1604
Passcode: 358199
Date: Thursday 27 January 2022
Time: 13:30 CET
Abstract:
Recent decades have witnessed a surge of psychological and neurological research at an individual level. One goal in such endeavors is to construct person-specific dynamic assessments using time series data. Within the psychometric field, researchers have developed psychometric modeling frameworks to estimate time series models. However, these methods are often limited in the dynamic representations as well as the model selection regimes. My dissertation research aims to evaluate (Chapter I), reconcile (Chapter II), and extend upon (Chapter III) the limitations in current practices. In this talk, I will focus on Chapter II, where I proposed a novel modeling approach that uses regularization under the unified Structure Equation Modeling (uSEM) framework to estimate a more flexible model, called regularized hybrid uSEM. My simulation study has shown that the proposed approach is more reliable and accurate than alternative methods in recovering hybrid types of dynamic relations and in eliminating spurious ones. The present work, to my knowledge, is the first application of the recent regularized SEM to the estimation of a type of time series SEM, which points to a promising future for statistical learning in psychometric models.
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References:
[1] Gates, K. M. & Molenaar, P. C. M. Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage 63, 310–319 (2012).
[2] Epskamp, S., Waldorp, L. J., Mõttus, R. & Borsboom, D. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res 53, 1–28 (2018).
[3] Ye, A., Gates, K. M., Henry, T. R. & Luo, L. Path and Directionality Discovery in Individual Dynamic Models: A Regularized Unified Structural Equation Modeling Approach for Hybrid Vector Autoregression. Psychometrika 86, 404–441 (2021).