Procedure for detecting outliers in a circular regression model

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Abstract

A number of circular regression models have been proposed in the literature. In recent years, there is a strong interest shown on the subject of outlier detection in circular regression. An outlier detection procedure can be developed by defining a new statistic in terms of the circular residuals. In this paper, we propose a new measure which transforms the circular residuals into linear measures using a trigonometric function. We then employ the row deletion approach to identify observations that affect the measure the most, a candidate of outlier. The corresponding cut-off points and the performance of the detection procedure when applied on Down and Mardia's model are studied via simulations. For illustration, we apply the procedure on circadian data.

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Rambli, A., Abuzaid, A. H. M., Bin Mohamed, I., & Hussin, A. G. (2016). Procedure for detecting outliers in a circular regression model. PLoS ONE, 11(4). https://doi.org/10.1371/journal.pone.0153074

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