Nowadays, estimation demand in statistics is increased worldwide to seek out an
estimate, or approximation, which may be a value which will be used for various
purpose, albeit the input data could also be incomplete, uncertain, or unstable. The
development of different estimation methods is trying to provide most accurate
estimate and estimation theory deals with finding estimates with good properties.
The demand of small area estimation (SAE) method has been increasing rapidly
around the world because of its reliability compared to the traditional direct
estimation methods, especially in the case of small sample size. This paper mainly
focuses on the comparison of several indirect small area estimation methods (poststratified synthetic, SSD and EB estimates) with traditional direct estimator based
on a renowned data set. Direct estimator is approximately unbiased but SSD and
Post-stratified synthetic estimator is extreme biased. To cope up the problem, we
conduct another model-based estimation procedure namely Empirical Bayes (EB)
estimator, which is unbiased and compare them using their coefficient of variation
(CV). To check the model assumption, we used Q-Q plot as well as a Histogram to
confirm the normality, bivariate correlation, Akaike information criterion (AIC).
JEL classification numbers: C13, C51, C51.
Keywords: Small Area Estimation, Direct Estimation, Indirect Estimation,
Empirical Bayes Estimator, Poverty Mapping.
Robust Empirical Bayes Small Area Estimation with Symmetric α-Stable Distribution for Error Components
