Consistent estimation and testing in comparing analytical bias models

Abstract

Regression techniques are commonly applied to compare two or more analytical methods at several concentration levels. The paper considers consistent estimation in measurement error models with known different variances typically used in such situations. The approach is based on the corrected score methodology, which allows the derivation of consistent estimators for the model parameters and also for the asymptotic covariance matrix of the parameter estimators. Thus, Wald type statistics are proposed for testing hypothesis related to the bias of the analytical methods with the asymptotic chi-square distribution, which guarantees correct asymptotic significance levels. Results of small-scale simulation studies are reported to illustrate comparisons with other approaches. Applications to real data sets are also considered. Copyright © 2005 John Wiley & Sons, Ltd.