Outlier detection in a circular regression model using COVRATIO statistic

Mohamed, I. and Ibrahim, S. and Rambli, A. and Hussin, A.G. (2013) Outlier detection in a circular regression model using COVRATIO statistic. Communications in Statistics - Simulation and Computation, 42 (10). pp. 2272-2280. ISSN 0361-0918, DOI https://doi.org/10.1080/03610918.2012.697239.

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In this article, we model the relationship between two circular variables using the circular regression models, to be called JS circular regression model, which was proposed by Jammalamadaka and Sarma (1993). The model has many interesting properties and is sensitive enough to detect the occurrence of outliers. We focus our attention on the problem of identifying outliers in this model. In particular, we extend the use of the COVRATIO statistic, which has been successfully used in the linear case for the same purpose, to the JS circular regression model via a row deletion approach. Through simulation studies, the cut-off points for the new procedure are obtained and its power of performance is investigated. It is found that the performance improves when the resulting residuals have small variance and when the sample size gets larger. An example of the application of the procedure is presented using a real dataset.

Item Type: Article
Uncontrolled Keywords: Circular regression; COVRATIO; Influential observation; Outlier; von Mises distribution
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms. Izzan Ramizah Idris
Date Deposited: 27 Oct 2014 05:08
Last Modified: 03 Jul 2017 08:28
URI: http://eprints.um.edu.my/id/eprint/10141

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