Asymptotic properties of an estimator in errors-in-variables models in the presence of validation data
Structural errors-in-variables models with dependent spatial observations are studied. The presence of validation data is assumed. An estimator for regression parameters proposed by Lee and Sepanski [1] is studied. Consistency and asymptotic normality of the estimator are established in the case of increasing domain. Infill asymptotic properties are described. Simulation results are also presented. (C) 1999 Elsevier Science Ltd. All rights reserved.
errors-in-variables validation measurement errors mixing property consistency of estimators random fields