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Statistic for Outlier Detection in Circular Functional Relationship Model

  • Mohd Syazwan Mohamad AnuarEmail author
  • Abdul Ghapor Hussin
  • Yong Zulina Zubairi
Conference paper

Abstract

During the last few years, researchers have shown strong interest on the subject of outlier detection in both linear and circular for Error-in-Variables (EIV) Models. Recently, the studies of outlier detection on circular variables models using row deletion method are widely explored; in particular in regression and EIV models for circular variables. In this paper, we have proposed a new measure of mean circular error using cosine function for circular functional relationship model. We also used the row deletion method to detect observations that affect the measure the most, thus identifying them as outlier. The corresponding cut-off points are identified via simulation studies.

Keywords

Circular functional relationship model Outlier Cosine function Row deletion 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Mohd Syazwan Mohamad Anuar
    • 1
    Email author
  • Abdul Ghapor Hussin
    • 2
  • Yong Zulina Zubairi
    • 3
  1. 1.Centre of Defense Foundation Studies, National Defense University of MalaysiaKuala LumpurMalaysia
  2. 2.Faculty of Defense Science and TechnologyNational Defense University of MalaysiaKuala LumpurMalaysia
  3. 3.Centre for Foundation Studies in Sciences, University of MalayaKuala LumpurMalaysia

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