A New Model Variance Estimator for an Area Level Small Area Model to Solve Multiple Problems Simultaneously (1701.04176v1)

Abstract: The two-level normal hierarchical model (NHM) has played a critical role in the theory of small area estimation (SAE), one of the growing areas in statistics with numerous applications in different disciplines. In this paper, we address major well-known shortcomings associated with the empirical best linear unbiased prediction (EBLUP) of a small area mean and its mean squared error (MSE) estimation by considering an appropriate model variance estimator that satisfies multiple properties. The proposed model variance estimator simultaneously (i) improves on the estimation of the related shrinkage factors, (ii) protects EBLUP from the common overshrinkage problem, (iii) avoids complex bias correction in generating strictly positive second-order unbiased mean square error (MSE) estimator either by the Taylor series or single parametric bootstrap method. The idea of achieving multiple desirable properties in an EBLUP method through a suitably devised model variance estimator is the first of its kind and holds promise in providing good inferences for small area means under the classical linear mixed model prediction framework. The proposed methodology is also evaluated using a Monte Carlo simulation study and real data analysis.