title: Procedure for Detecting Outliers in a Circular Regression Model.
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.
authors.affiliation: Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Adzhar
authors.initials: A
authors.lastname: Rambli
authors.affiliation: Department of Mathematics, Faculty of Science, Al-Azhar University-Gaza, Palestine.
authors.forename: Ali H M
authors.initials: AH
authors.lastname: Abuzaid
authors.affiliation: Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Ibrahim
authors.initials: I
authors.lastname: Bin Mohamed
authors.affiliation: Centre for Defence Foundation Studies, National Defence University of Malaysia, Kuala Lumpur, Malaysia.
authors.forename: Abdul Ghapor
authors.initials: AG
authors.lastname: Hussin
mesh_terms: D000465:Algorithms; D001795:Blood Pressure Determination; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D012044:Regression Analysis; D013223:Statistics as Topic; D013337:Students, Medical