Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data

• Published in: Psychometrika Vol. 87; no. 2; pp. 1 - 29
• Main Authors: Fisher, Zachary F., Kim, Younghoon, Fredrickson, Barbara L., Pipiras, Vladas
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2022; Springer Nature B.V
• Subjects: Algorithms; Assessment; Behavioral Science and Psychology; Behavioral sciences; Forecasting; Forecasting with Intensive Longitudinal Data; Humanities; Humans; Law; Physiology; Psychology; Psychometrics; Research Design; Social sciences; Statistical Theory and Methods; Statistics for Social Sciences; Testing and Evaluation; Theory and Methods; Time series; Variables; ILD; vector autoregression; forecasting; proximal gradient descent; regularization; LASSO; time series

Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this...