Discriminant analysis of environmental data based on zero inflated spatial auto-beta models

Abstract

In this paper, a supervised classification problem of classifying feature observation into one of two populations is considered. Classical supervised classification in this work is expanded for non-Gaussian spatial data specified by zero inflated auto-beta models. A classification rule based on the Bayes discriminant function (BDF) that uses conditional probability density functions in the expression is proposed. This rule is applied to real data set for identifying sea bottom type by using Black carrageen concentration data over the southeastern Baltic Sea. Different feature observation models are chosen and classification results are compared using a hold-out error rate measure.