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On detecting outlier in simple linear functional relationship model using covratio statistic.

Author(s)
A. A. Ghapor Centre for Foundation Studies in Science, University of Malaya Kuala Lumpur, Malaysia
Y. Z. Zubairi Centre for Foundation Studies in Science, University of Malaya Kuala Lumpur, Malaysia
A. S. M. A. Mamun Centre for Foundation Studies in Science, University of Malaya Kuala Lumpur, Malaysia
A. H. M. R. Imon Department of Mathematical Sciences, Ball State University, Indiana, USA
Abstract
Outlier detection is a concern in data analysis, as anomalous observation can have consequence pertaining to the inference made from the analysis. Numerous diagnostic tools have developed especially for regression model to overcome the presence of contamination in data. In this paper, we derived a test statistic using COVRATIO to detect outliers in linear functional relationship model. The cut-off point for the statistic is determined using simulation result. We found that the power of performance for detecting the outlier increases as the standard deviation of error variance decreases and it is independent of the sample size. Finally, this method is illustrated using simulated and real data set.
Publication Details
Page(s) 129-142
DOI DOI not available
Published Journal: Pakistan Journal of Statistics, Volume: 30, Issue: 1, Year: 2014

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