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<p>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.</p>

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Low-bias discrimination of circular data with measurement errors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月18日

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  • Marco Di Marzio,
  • Stefania Fensore,
  • Agnese Panzera,
  • Chiara Passamonti

摘要

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.