Some recent statistical learning methods for longitudinal high‐dimensional data. (13th December 2013)

• Record Type: Journal Article
• Authors: Chen, Shuo; Grant, Edward; Wu, Tong Tong; Bowman, F. DuBois
• Is Part Of: Wiley interdisciplinary reviews. Volume 6:Number 1(2014)
• Page Start: 10
• Page End: 18
• Publication Date: 2013-12-13
• Subjects: high dimensionality -- multiple times points -- prediction -- support vector machines -- shrinkage -- temporal effects; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1282
• Languages: English
• ISSNs: 1939-5108

Abstract

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. WIREs Comput Stat 2014, 6:10–18. doi: 10.1002/wics.1282 This article is categorized under: Software for Computational Statistics > Artificial Intelligence and Expert Systems Statistical Learning and Exploratory Methods of the Data Sciences > Support Vector Machines