ANOCOVA models with measurement errors

This paper deals with analysis of covariance (ANOCOVA) models in a completely randomized design where the explanatory variables are measured with error. The structural models considered encompasses both multiplicative and additive errors in the explanatory variables. Two consistent estimators of the treatment effects are compared in terms of asymptotic relative efficiency. It is shown that the naive estimator which ignores measurement errors is the one that presents the best behavior. A simulation study compares the relative merits of the estimators in finite samples.