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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Vydáno v:	Psychometrika Ročník 87; číslo 2; s. 1 - 29
Hlavní autoři:	Fisher, Zachary F., Kim, Younghoon, Fredrickson, Barbara L., Pipiras, Vladas
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.06.2022 Springer Nature B.V
Témata:	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
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Shrnutí:	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 gap in the literature, we present the multi-VAR framework , a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals. Importantly, our approach estimates models for all individuals simultaneously and is capable of adaptively adjusting to the amount of heterogeneity present across individual dynamic processes. To accomplish this, we propose a novel proximal gradient descent algorithm for solving the multi-VAR problem and prove the consistency of the recovered transition matrices. We evaluate the forecasting performance of our method in comparison with a number of benchmark methods and provide an illustrative example involving the day-to-day emotional experiences of 16 individuals over an 11-week period.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0033-3123 1860-0980 1860-0980
DOI:	10.1007/s11336-021-09825-7