Supervised linear classification of Gaussian spatio-temporal data
Articles
Marta Karaliutė
Vilnius University
Kęstutis Dučinskas
Klaipeda University
https://orcid.org/0000-0002-6079-7504
Published 2021-12-15
https://doi.org/10.15388/LMR.2021.25214
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Keywords

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.

Abstract

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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