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While multivariate circular data are emerging from various disciplines in recent studies, only a few outlier detection methods are found in the literature. In this paper, we provide two non-parametric outlier detection methods, where we employ a data depth technique and a Pearson product-moment type of closeness measure. In addition, we present a non-parametric goodness of fit test based on the Rosenblatt transformation for a copula-based multivariate circular distribution (Kim et al., 2016). Our proposed methods were illustrated using a real data set arising from the problem of protein structure prediction.

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Non-Parametric Diagnostic Methods for Detecting Outliers in Multivariate Circular Data

  • 章节

  • 发表日期：

    2025年

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  • Sungsu Kim,
  • Rahul Chatterjee

摘要

While multivariate circular data are emerging from various disciplines in recent studies, only a few outlier detection methods are found in the literature. In this paper, we provide two non-parametric outlier detection methods, where we employ a data depth technique and a Pearson product-moment type of closeness measure. In addition, we present a non-parametric goodness of fit test based on the Rosenblatt transformation for a copula-based multivariate circular distribution (Kim et al., 2016). Our proposed methods were illustrated using a real data set arising from the problem of protein structure prediction.