Application of outliers detection techniques in nonlinear regression
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Outliers, Nonlinear Regression, Linear Approximation, Design Matrix
Abstract
outlier's detection is very essential issue due to their responsibility for producing interpretative problem in linear as well as in nonlinear regression analysis. Much work has been accomplished on the identification of outlier in linear regression, but not as in nonlinear regression. This paper aims to evaluate several outlier detection techniques for nonlinear regression based on Studentized Residuals, Hadi Potential, Cook Distance, Difference in Fits and Atkinson's Distance). The main idea is to use the linear approximation of a nonlinear model and consider the gradient as the design matrix. Subsequently, the detection techniques are formulated. A real life data showed that among the five measures, only Difference in Fits and Cook Distance consistently capable of identifying the correct outlier.
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