Strong consistency of a kernel-based rule for spatially dependent data

Abstract

We consider the kernel-based classifier proposed by Younso (2017). This nonparametric classifier allows for the classification of missing spatially dependent data. The weak consistency of the classifier has been studied by Younso (2017). The purpose of this paper is to establish strong consistency of this classifier under mild conditions. The classifier is discussed in a multi-class case. The results are illustrated with simulation studies and real applications.

Keywords

• Bayes rule
• Kernel rule
• Random field
• Bandwidth
• Strong consistency