Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data

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...

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Veröffentlicht in:	Psychometrika Jg. 87; H. 2; S. 1 - 29
Hauptverfasser:	Fisher, Zachary F., Kim, Younghoon, Fredrickson, Barbara L., Pipiras, Vladas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2022 Springer Nature B.V
Schlagworte:	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
ISSN:	0033-3123, 1860-0980, 1860-0980
Online-Zugang:	Volltext
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