Kernel density classification for spherical data

Classifying observations coming from two different spherical populations by using a nonparametric method appears to be an unexplored field, although clearly worth to pursue. We propose some decision rules based on spherical kernel density estimation and we provide asymptotic L₂ properties. A real-data application using global climate data is finally discussed