Application of spatial auto-beta models in statistical classification

Abstract

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.

Keywords

• Bayes discriminant function
• linear discriminant function
• actual error rate
• supervised classification