Procedure for Detecting Outliers in a Circular Regression Model

PLoS One. 2016 Apr 11;11(4):e0153074. doi: 10.1371/journal.pone.0153074. eCollection 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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Blood Pressure Determination
  • Computer Simulation
  • Humans
  • Models, Theoretical*
  • Regression Analysis*
  • Statistics as Topic*
  • Students, Medical

Grants and funding

University of Malaya Research Grant Scheme (no. RP009C-13AFR) - website: http://umresearch.um.edu.my. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.