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Procedure for Detecting Outliers in a Circular Regression Model.
- Source :
- PLoS ONE; 4/11/2016, Vol. 11 Issue 4, p1-10, 10p
- Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 114462506
- Full Text :
- https://doi.org/10.1371/journal.pone.0153074