Robust Fitting of a Wrapped Normal Model to Multivariate Circular Data and Outlier Detection
| Autor: | Giovanni Saraceno, Claudio Agostinelli, Luca Greco |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multivariate statistics
Computer science Inference Wrapped normal distribution Estimating equations 01 natural sciences Set (abstract data type) 010104 statistics & probability 0502 economics and business 0101 mathematics mahalanobis distance 050205 econometrics MCD weighted likelihood Mahalanobis distance Statistics 05 social sciences MM-estimation General Medicine classification EM HA1-4737 Outlier Anomaly detection Algorithm |
| Zdroj: | Stats, Vol 4, Iss 28, Pp 454-471 (2021) Stats Volume 4 Issue 2 Pages 28-471 |
| ISSN: | 2571-905X |
| DOI: | 10.3390/stats4020028 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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