Supervised linear classification of Gaussian spatio-temporal data
Marta Karaliutė
Vilnius University
Kęstutis Dučinskas
Klaipeda University
Published 2021-12-15


separable covariance function
AR(p) model
Bayes discriminant function

How to Cite

Karaliutė M. and Dučinskas K. (2021) “Supervised linear classification of Gaussian spatio-temporal data”, Lietuvos matematikos rinkinys, 62(A), pp. 9-15. doi: 10.15388/LMR.2021.25214.


In this article we focus on the problem of supervised classifying of the spatio-temporal Gaussian random field observation into one of two classes, specified by different mean parameters. The main distinctive feature of the proposed approach is allowing the class label to depend on spatial location as well as on time moment. It is assumed that the spatio-temporal covariance structure factors into a purely spatial component and a purely temporal component following AR(p) model. In numerical illustrations with simulated data, the influence of the values of spatial and temporal covariance parameters to the derived error rates for several prior probabilities models are studied.

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