performance of ridge regression approach in linear measurement error models with replicated data

It is well known that bias in parameter estimates arises when there are measurement errors in the covariates of regression models. one solution for decreasing such biases is the use of prior information concerning the measurement error, which is often called replication data. in this paper, we present a ridge estimator in replicated measurement error (rmer) to overcome the multicollinearity problem in such models. the performance of rmer against some other estimators is investigated. large sample properties of our estimator are derived and compared with other estimators using a simulation study as well as a real data set.