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Article
Classification with the matrix-variate-t distribution
Journal of Computational and Graphical Statistics
  • Geoffrey Z. Thompson, Iowa State University
  • Ranjan Maitra, Iowa State University
  • William Q. Meeker, Iowa State University
  • Ashraf F. Bastawros, Iowa State University
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-22-2020
DOI
10.1080/10618600.2019.1696208
Abstract

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.

Comments

This is a manuscript of an article published as Thompson, Geoffrey Z., Ranjan Maitra, William Q. Meeker, and Ashraf F. Bastawros. "Classification with the matrix-variate-t distribution." Journal of Computational and Graphical Statistics 29, no. 3 (2020): 668-674. doi:10.1080/10618600.2019.1696208. Posted with permission.

Copyright Owner
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Language
en
File Format
application/pdf
Citation Information
Geoffrey Z. Thompson, Ranjan Maitra, William Q. Meeker and Ashraf F. Bastawros. "Classification with the matrix-variate-t distribution" Journal of Computational and Graphical Statistics Vol. 29 Iss. 3 (2020) p. 668 - 674
Available at: http://works.bepress.com/wqmeeker/148/