  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.
