Campus Units
Aerospace Engineering, Materials Science and Engineering, Mechanical Engineering, Statistics
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-22-2020
Journal or Book Title
Journal of Computational and Graphical Statistics
Volume
29
Issue
3
First Page
668
Last Page
674
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.
Copyright Owner
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Copyright Date
2019
Language
en
File Format
application/pdf
Recommended Citation
Thompson, Geoffrey Z.; Maitra, Ranjan; Meeker, William Q.; and Bastawros, Ashraf F., "Classification with the matrix-variate-t distribution" (2020). Aerospace Engineering Publications. 148.
https://lib.dr.iastate.edu/aere_pubs/148
Included in
Multivariate Analysis Commons, Statistical Methodology Commons, Statistical Models Commons
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.