Seminar in Psychometrics

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Path and Directionality Discovery in Individual Dynamic Models: A Regularized Structural Equation Modeling Approach

Date and time: January 27, 2022 (1:30 PM CET)
Place Jointly held as seminar of ISCB - ČR and remotely on ZOOM

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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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). doi: 10.1007/s11336-021-09753-6

Ai Ye,
UNC Chapel Hill