The classical maximum likelihood estimation fails to estimate the simple linear measurement error model, with or without equation error, unless additional assumptions are made about the structural parameters. In the literature there are six different assumptions that could be added in order to solve the measurement error models. In this paper, we proposed an entropy-type estimator based on the generalized maximum entropy estimation approach, which allows one to abstract away from the additional assumptions that are made in the classical method. Monte Carlo experiments were carried out in order to investigate the performance of the proposed estimators. The simulation results showed that the entropy-type estimator of unknown parameters has outperformed the classical estimators in terms of mean square error criterion.
Maximum Lq-likelihood Estimation in Functional Measurement Error Models
