Some recent statistical learning methods for longitudinal high-dimensional data
Article first published online: 13 DEC 2013
© 2013 Wiley Periodicals, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 6, Issue 1, pages 10–18, January/February 2014
How to Cite
Chen, S., Grant, E., Wu, T. T. and Bowman, F. D. (2014), Some recent statistical learning methods for longitudinal high-dimensional data. WIREs Comp Stat, 6: 10–18. doi: 10.1002/wics.1282
- Issue published online: 23 DEC 2013
- Article first published online: 13 DEC 2013
- Manuscript Accepted: 18 OCT 2013
- Manuscript Revised: 24 SEP 2013
- Manuscript Received: 23 MAY 2013
- NSF. Grant Number: CCF-0926181
- high dimensionality;
- multiple times points;
- support vector machines;
- temporal effects
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
Conflict of interest: The authors have declared no conflicts of interest for this article.
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