Low-bias discrimination of circular data with measurement errors

Abstract We study nonparametric discrimination among circular density populations when sample data are affected by measurement errors. Relatively little research seems to have been devoted to this topic. Notoriously, in these problems, a nonparametric method needs to account for an additional source of bias due to the presence of measurement errors, beyond the usual bias typical of local methods. In the described context of abundant bias, we propose a deconvolution approach involving lower bias kernel estimators. Some asymptotic properties are discussed, and numerical results are provided along with a real data case study.