Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data

DOI10.48550/ARXIV.2007.05052zbMath1490.62404arXiv2007.05052OpenAlexW3042169291MaRDI QIDQ107040

Younghoon Kim, Barbara Fredrickson, Vladas Pipiras, Zachary Fisher, Vladas Pipiras, Zachary Fisher, Young-Hoon Kim, Barbara L. Fredrickson

Publication date: 9 July 2020

Published in: Psychometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2007.05052

zbMATH Keywords

regularizationtime seriesforecastingLassovector autoregressionILDproximal gradient descent

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to psychology (62P15)

Related Items (2)

Guest editors' introduction to the special issue on forecasting with intensive longitudinal data ⋮ multivar

Uses Software

• glmnet
• gimme
• multivar

Cites Work

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• Forecasting Using Principal Components From a Large Number of Predictors
• Regression modelling on stratified data with the lasso
• An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias

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