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.

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

Published Version

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