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In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain whether they are influential or not. Different influential statistics including Cook’s Distance, Welsch-Kuh distance and DFBETAS have been proposed. Based on these influential statistics, the use of some robust estimators MM, Least trimmed square LTS and S is proposed and considered as alternative to influential statistics based on the robust estimator M and the ordinary least square OLS. The statistics based on these estimators were applied into three set of data and the root mean square error RMSE was used as a criterion to compare the estimators. Generally, influential measures are mostly efficient with M or MM robust estimators.

KEYWORDS

DFFITS, Cook’s D, DFBETAS, OLS, RMSE

Cite this paper

Ayinde, K. , Lukman, A. and Arowolo, O. 2015 Robust Regression Diagnostics of Influential Observations in Linear Regression Model. Open Journal of Statistics, 5, 273-283. doi: 10.4236-ojs.2015.54029.





Autor: Kayode Ayinde1, Adewale F. Lukman1, Olatunji Arowolo2

Fuente: http://www.scirp.org/



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