Open AccessEvaluating the Approaches of Small Area Estimation Using Poverty Mapping Data
Author(s) -
Mizanur Rahman,
Deluar J. Moloy,
Md. Sifat Ar Salan
Publication year - 2021
Publication title -
journal of statistical and econometric methods
Language(s) - English
DOI - 10.47260/jsem/1021
Subject(s) - small area estimation , estimator , statistics , mathematics , estimation , bayes estimator , akaike information criterion , bayes' theorem , mean squared error , econometrics , bias of an estimator , minimum variance unbiased estimator , bayesian probability , management , economics
Nowadays, estimation demand in statistics is increased worldwide to seek out anestimate, or approximation, which may be a value which will be used for variouspurpose, albeit the input data could also be incomplete, uncertain, or unstable. Thedevelopment of different estimation methods is trying to provide most accurateestimate and estimation theory deals with finding estimates with good properties.The demand of small area estimation (SAE) method has been increasing rapidlyaround the world because of its reliability compared to the traditional directestimation methods, especially in the case of small sample size. This paper mainlyfocuses on the comparison of several indirect small area estimation methods (poststratified synthetic, SSD and EB estimates) with traditional direct estimator basedon a renowned data set. Direct estimator is approximately unbiased but SSD andPost-stratified synthetic estimator is extreme biased. To cope up the problem, weconduct 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 toconfirm 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.