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

Sungsu Kim () and Rahul Chatterjee
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Sungsu Kim: University of Wisconsin-Green Bay, Resch School of Engineering
Rahul Chatterjee: University of Louisiana at Lafayette, Department of Mathematics

A chapter in Directional and Multivariate Statistics, 2025, pp 147-158 from Springer

Abstract: 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.

Keywords: Data depth; Goodness of fit test; Multivariate circular data; Outlier detection method; Protein structure prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-2004-3_8

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DOI: 10.1007/978-981-96-2004-3_8

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