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Identifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation Technique

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DOI: 10.4236/ojs.2014.41003    9,087 Downloads   15,995 Views   Citations

ABSTRACT

The use of [1] Box-Cox power transformation in regression analysis is now common; in the last two decades there has been emphasis on diagnostics methods for Box-Cox power transformation, much of which has involved deletion of influential data cases. The pioneer work of [2] studied local influence on constant variance perturbation in the Box-Cox unbiased regression linear mode. Tsai and Wu [3] analyzed local influence method of [2] to assess the effect of the case-weights perturbation on the transformation-power estimator in the Box-Cox unbiased regression linear model. Many authors noted that the influential observations on the biased estimators are different from the unbiased estimators. In this paper I describe a diagnostic method for assessing the local influence on the constant variance perturbation on the transformation in the Box-Cox biased ridge regression linear model. Two real macroeconomic data sets are used to illustrate the methodologies.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Jahufer, "Identifying Unusual Observations in Ridge Regression Linear Model Using Box-Cox Power Transformation Technique," Open Journal of Statistics, Vol. 4 No. 1, 2014, pp. 19-26. doi: 10.4236/ojs.2014.41003.

References

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