1. Procedure for Detecting Outliers in a Circular Regression Model.
- Author
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Rambli A, Abuzaid AH, Bin Mohamed I, and Hussin AG
- Subjects
- Blood Pressure Determination, Computer Simulation, Humans, Students, Medical, Algorithms, Models, Theoretical, Regression Analysis, Statistics as Topic
- 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.
- Published
- 2016
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