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A clustering approach to detect multiple outliers in linear functional relationship model for circular data

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  • Nurkhairany Amyra Mokhtar
  • Yong Zulina Zubairi
  • Abdul Ghapor Hussin

Abstract

Outlier detection has been used extensively in data analysis to detect anomalous observation in data. It has important applications such as in fraud detection and robust analysis, among others. In this paper, we propose a method in detecting multiple outliers in linear functional relationship model for circular variables. Using the residual values of the Caires and Wyatt model, we applied the hierarchical clustering approach. With the use of a tree diagram, we illustrate the detection of outliers graphically. A Monte Carlo simulation study is done to verify the accuracy of the proposed method. Low probability of masking and swamping effects indicate the validity of the proposed approach. Also, the illustrations to two sets of real data are given to show its practical applicability.

Suggested Citation

  • Nurkhairany Amyra Mokhtar & Yong Zulina Zubairi & Abdul Ghapor Hussin, 2018. "A clustering approach to detect multiple outliers in linear functional relationship model for circular data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 1041-1051, April.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:6:p:1041-1051
    DOI: 10.1080/02664763.2017.1342779
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