title: Statistical Learning Methods for Longitudinal High-dimensional Data.
abstract: Recent studies have collected high-dimensional data longitudinally. Examples include brain images collected during different scanning sessions and time-course gene expression data. Because of the additional information learned from the temporal changes of the selected features, such longitudinal high-dimensional data, when incorporated with appropriate statistical learning techniques, are able to more accurately predict disease status or responses to a therapeutic treatment. In this article, we review recently proposed statistical learning methods dealing with longitudinal high-dimensional data.
authors.affiliation: Department of Epidemiology and Biostatistics, University of Maryland, College Park, 20742.
authors.forename: Shuo
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Epidemiology and Biostatistics, University of Maryland, College Park, 20742.
authors.forename: Edward
authors.initials: E
authors.lastname: Grant
authors.affiliation: Department of Epidemiology and Biostatistics, University of Maryland, College Park, 20742.
authors.forename: Tong Tong
authors.initials: TT
authors.lastname: Wu
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322.
authors.forename: F DuBois
authors.initials: FD
authors.lastname: Bowman
keywords: High-dimensionality; Multiple times points; Prediction; Shrinkage; Support vector machines; Temporal effects