Aerospace Engineering, Materials Science and Engineering, Mechanical Engineering, Statistics
Journal or Book Title
Journal of Computational and Graphical Statistics
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.
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Thompson, Geoffrey Z.; Maitra, Ranjan; Meeker, William Q.; and Bastawros, Ashraf F., "Classification with the matrix-variate-t distribution" (2020). Aerospace Engineering Publications. 148.