Application of spatial auto-beta models in statistical classification
Eglė Zikarienė
Vilnius University
Kęstutis Dučinskas
Klaipeda University
Published 2021-12-15


Bayes discriminant function
linear discriminant function
actual error rate
supervised classification

How to Cite

Zikarienė E. and Dučinskas K. (2021) “Application of spatial auto-beta models in statistical classification”, Lietuvos matematikos rinkinys, 62(A), pp. 36-43. doi: 10.15388/LMR.2021.25219.


In this paper, spatial data specified by auto-beta models is analysed by considering a supervised classification problem of classifying feature observation into one of two populations. Two classification rules based on conditional Bayes discriminant function (BDF) and linear discriminant function (LDF) are proposed. These classification rules are critically compared by the values of the actual error rates through the simulation study.

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