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Detection of outliers in simple circular regression models using the mean circular error statistic

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dc.contributor.author Abuzaid, Ali
dc.contributor.author Hussin, AG
dc.contributor.author Mohamed ·, IB
dc.date.accessioned 2019-11-24T18:47:31Z
dc.date.available 2019-11-24T18:47:31Z
dc.date.issued 2013-02-01
dc.identifier.citation A. H. Abuzaid, A. G. Hussin & I. B. Mohamed (2013) Detection of outliers in simple circular regression models using the mean circular error statistic, Journal of Statistical Computation and Simulation, 83:2, 269-277, DOI: 10.1080/00949655.2011.602679 en_US
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/516
dc.description.abstract The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated. It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set. en_US
dc.language.iso en_US en_US
dc.publisher Journal of Statistical Computation and Simulation en_US
dc.subject circular distance, circular regression model, mean circular error, outlier, row deletion en_US
dc.title Detection of outliers in simple circular regression models using the mean circular error statistic en_US
dc.type Article en_US


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