Some recent statistical learning methods for longitudinal high-dimensional data

• Type of Material: Article
• Authors: Shuo Chen, University of Maryland; Edward Grant, Uniformed Services University; Tong Tong Wu, University of Rochester; Frederick Bowman, Emory University
• Language: English
• Date: 2014-01-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/wics.1282
• Title of Journal or Parent Work: Wiley Interdisciplinary Reviews: Computational Statistics
• ISSN: 1939-5108
• Volume: 6
• Issue: 1
• Start Page: 10
• End Page: 18
• Grant/Funding Information: This article was partially supported by NIH grant U18 NS082143‐01.; Wu's research was supported in part by NSF Grant CCF‐0926181.
• Keywords: Temporal effects; High-dimensionality; Shrinkage; Multiple times points; Support vector machines; Prediction
• Research Categories: Biology, Bioinformatics; Biology, Biostatistics

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 into 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.