Procedure for Detecting Outliers in a Circular Regression Model
Adzhar Rambli1, Ali H M Abuzaid2, Ibrahim Bin Mohamed1
1Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia.
Plos One
|April 12, 2016
Summary
This study introduces a novel method for detecting outliers in circular regression by transforming circular residuals into linear measures. The new procedure effectively identifies influential observations in circular regression models, demonstrated with simulations and circadian data analysis.
Area of Science:
- Statistics
- Circular Data Analysis
Background:
- Circular regression models are increasingly important.
- Outlier detection in these models is a significant challenge.
Purpose of the Study:
- To propose a new method for outlier detection in circular regression.
- To develop a statistic based on transformed circular residuals.
Main Methods:
- Transforming circular residuals into linear measures using trigonometric functions.
- Employing the row deletion approach to identify influential observations.
- Conducting simulations to evaluate the procedure's performance.
Main Results:
- A new outlier detection measure for circular regression is proposed.
- The method successfully identifies candidate outliers.
- Performance is evaluated via simulations on Down and Mardia's model.
Conclusions:
- The proposed method offers a viable approach for outlier detection in circular regression.
- The procedure is illustrated using real-world circadian data.
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