Asymptotic covariance and detection of influential observations in a linear functional relationship model for circular data with application to the measurements of wind directions

Hussin, A.G. and Abuzaid, A. and Zulkifili, F. and Mohamed, I. (2010) Asymptotic covariance and detection of influential observations in a linear functional relationship model for circular data with application to the measurements of wind directions. Scienceasia, 36 (3). pp. 249-253.

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Abstract

This paper discusses the asymptotic covariance and outlier detection procedure in a linear functional relationship model for an extended circular model proposed by Caires and Wyatt. We derive the asymptotic covariance matrix of the model via the Fisher information and use the results to detect influential observations in the model. Consequently, an influential observation detection procedure is developed based on the COVRATIO statistic which has been widely used for similar purposes in ordinary linear regression models. We show via simulation that the above procedure performs well in detecting influential observations. As an illustration, the procedure is applied to the real data of the wind direction measured by two different instruments.

Item Type: Article
Uncontrolled Keywords: Circular variables; error of concentration parameters; maximum likelihood estimation
Subjects: R Medicine
Depositing User: Mr Faizal 2
Date Deposited: 30 Jan 2015 03:06
Last Modified: 30 Jan 2015 03:06
URI: http://eprints.um.edu.my/id/eprint/12507

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