EconPapers    
Economics at your fingertips  
 

Tensor Regression with Applications in Neuroimaging Data Analysis

Hua Zhou, Lexin Li and Hongtu Zhu

Journal of the American Statistical Association, 2013, vol. 108, issue 502, 540-552

Abstract: Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data. Supplementary materials for this article are available online.

Date: 2013
References: View complete reference list from CitEc
Citations View citations in EconPapers (4) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.776499 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:108:y:2013:i:502:p:540-552

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2018-04-14
Handle: RePEc:taf:jnlasa:v:108:y:2013:i:502:p:540-552