A two-parameter estimator in linear measurement error model

This article is concerned with the parameter estimation in linear measurement error model when there is ill-conditioned data. To deal with the multicollinearity problem, a new two-parameter estimator is proposed. The asymptotic properties of the new estimator are considered using the mean squared error matrix. Finally, a Monte Carlo simulation is presented to show the performances of the estimators in terms of simulated mean squared error criteria. According to the results, the new estimator can be suggested as an alternative to the other existing estimators in the presence of ill-conditioned data.

Automated summary

This article discusses parameter estimation in a linear measurement error model with ill-conditioned data and proposes a new two-parameter estimator to address the problem of multicollinearity. The asymptotic properties of the new estimator are considered using the mean squared error matrix. Finally, a Monte Carlo simulation is conducted to demonstrate the performance of the estimators based on simulated mean squared error criteria.