Assessing the efficiency of weighted mixed Liu estimator in linear measurement errors
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Abstract
This article introduces the weighted mixed Liu estimator for linear measurement error models. Additionally, the analysis incorporates stochastic linear restrictions along with the presence of multicollinearity. The proposed estimator facilitates the allocation of varying weights to the auxiliary information in relation to sample information. The asymptotic properties of the resultant estimator are established, and the efficacy of various estimators is assessed utilizing the mean squared error matrix criterion. A simulation study and numerical example are provided to assess the theoretical results. The simulation results and numerical example indicate that the proposed estimator outperforms the existing estimators.
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