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
(post-stratified 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.