Affiliations 

  • 1 Institute of Mathematical Sciences, University of Malaya, Kuala Lumpur, Malaysia
  • 2 Department of Mathematics, Faculty of Science, Al-Azhar University-Gaza, Palestine
  • 3 Centre for Defence Foundation Studies, National Defence University of Malaysia, Kuala Lumpur, Malaysia
PLoS One, 2016;11(4):e0153074.
PMID: 27064566 DOI: 10.1371/journal.pone.0153074

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.