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

Luca Greco, Giovanni Saraceno and Claudio Agostinelli
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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

Stats, 2021, vol. 4, issue 2, 1-18

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.

Keywords: classification; EM; mahalanobis distance; MCD; MM-estimation; weighted likelihood (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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