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Robust Fitting of a Wrapped Normal Model to Multivariate Circular Data and Outlier Detection

Author

Listed:
  • Luca Greco

    (University Giustino Fortunato, 82100 Benevento, Italy)

  • Giovanni Saraceno

    (Department of Mathematics, University of Trento, 38122 Trento, Italy)

  • Claudio Agostinelli

    (Department of Mathematics, University of Trento, 38122 Trento, Italy)

Abstract

In this work, we deal with a robust fitting of a wrapped normal model to multivariate circular data. Robust estimation is supposed to mitigate the adverse effects of outliers on inference. Furthermore, the use of a proper robust method leads to the definition of effective outlier detection rules. Robust fitting is achieved by a suitable modification of a classification-expectation-maximization algorithm that has been developed to perform a maximum likelihood estimation of the parameters of a multivariate wrapped normal distribution. The modification concerns the use of complete-data estimating equations that involve a set of data dependent weights aimed to downweight the effect of possible outliers. Several robust techniques are considered to define weights. The finite sample behavior of the resulting proposed methods is investigated by some numerical studies and real data examples.

Suggested Citation

  • Luca Greco & Giovanni Saraceno & Claudio Agostinelli, 2021. "Robust Fitting of a Wrapped Normal Model to Multivariate Circular Data and Outlier Detection," Stats, MDPI, vol. 4(2), pages 1-18, June.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:2:p:28-471:d:566925
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    References listed on IDEAS

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    Cited by:

    1. Greco, Luca & Pacillo, Simona & Maresca, Piera, 2023. "An impartial trimming algorithm for robust circle fitting," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

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