Local influence in regression models for the detection of analytical bias

This work deals with errors-in-variables regression models applied in comparing two analytical methods. The chief aim of the paper is the assessment of effects of minor perturbations of data on test statistics. To accomplish this we resort to methods of local influence. The techniques provide to the analyst a valuable tool that enables to identify potential influential elements and to quantify the effects of perturbations on test statistics. An illustrative example with a real data set is also reported.