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

In: Directional and Multivariate Statistics

Author

Listed:
  • Sungsu Kim

    (University of Wisconsin-Green Bay, Resch School of Engineering)

  • Rahul Chatterjee

    (University of Louisiana at Lafayette, Department of Mathematics)

Abstract

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.

Suggested Citation

  • Sungsu Kim & Rahul Chatterjee, 2025. "Non-Parametric Diagnostic Methods for Detecting Outliers in Multivariate Circular Data," Springer Books, in: Somesh Kumar & Barry C. Arnold & Kunio Shimizu & Arnab Kumar Laha (ed.), Directional and Multivariate Statistics, pages 147-158, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-2004-3_8
    DOI: 10.1007/978-981-96-2004-3_8
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