Geodesic
Generalized Mahalanobis distance and its application in detecting matrix outliers
Filomat, Tome 37 (2023) no. 23, p. 7993

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DOI

In this paper, a new distance for matrix observations called generalized Mahalanobis distance is introduced, some of its properties are studied, and its distribution is obtained for the observations of the matrix variate elliptically contoured distributions. Also, as a significant application, the introduced distance is used in detecting matrix outliers, and its method is described. Finally, some examples are provided for illustrative purposes, and the performance of the presented approach of detecting outliers is investigated by a simulation study.
DOI : 10.2298/FIL2323993R
Classification : 65F35, 62H10
Keywords: Mahalanobis distance, Matrix variate distribution, Outlier detection, Minimum covariance determinant estimator 
Amir Rezaei; Kambiz Ahmadi. Generalized Mahalanobis distance and its application in detecting matrix outliers. Filomat, Tome 37 (2023) no. 23, p. 7993 . doi: 10.2298/FIL2323993R
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     title = {Generalized {Mahalanobis} distance and its application in detecting matrix outliers},
     journal = {Filomat},
     pages = {7993 },
     year = {2023},
     volume = {37},
     number = {23},
     doi = {10.2298/FIL2323993R},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2323993R/}
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