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

Mokhtar, Nurkhairany Amyra and Zubairi, Yong Zulina and Hussin, Abdul Ghapor (2018) A clustering approach to detect multiple outliers in linear functional relationship model for circular data. Journal of Applied Statistics, 45 (6). pp. 1041-1051. ISSN 0266-4763

Full text not available from this repository.
Official URL: https://doi.org/10.1080/02664763.2017.1342779

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.

Item Type: Article
Uncontrolled Keywords: Linear functional relationship model; clustering; outliers detection; wind data; circular variables
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Centre for Foundation Studies in Science > Mathematics Division
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 May 2019 06:18
Last Modified: 14 May 2019 06:18
URI: http://eprints.um.edu.my/id/eprint/21223

Actions (login required)

View Item View Item