Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients

DOI10.1016/j.csda.2018.10.002zbMath1471.62229OpenAlexW2898001576WikidataQ129113187 ScholiaQ129113187MaRDI QIDQ1615281

Mu Yue, Ming-Yen Cheng, Jia-Liang Li

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2018.10.002

zbMATH Keywords

longitudinal dataminimum description lengthvariable selectionvarying-coefficient modelsparse boosting

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (4)

Editorial for the special issue on high-dimensional and functional data analysis ⋮ A new approach of subgroup identification for high-dimensional longitudinal data ⋮ Conditional sparse boosting for high-dimensional instrumental variable estimation ⋮ A New Functional Estimation Procedure for Varying Coefficient Models

Uses Software

• glasso
• AdaBoost.MH
• erboost

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