Hierarchical Bayes Small Area Estimation under a Unit Level Model with Applications in Agriculture

Abstract

To studied Bayesian aspect of small area estimation using Unit level model. In this paper we proposed and evaluated new prior distribution for the ratio of variance components in unit level model rather than uniform prior. To approximate the posterior moments of small area means, Laplace approximation method is applied. This choice of prior avoids the extreme skewness, usually present in the posterior distribution of variance components. This property leads to more accurate Laplace approximation. We apply the proposed model to the analysis of horticultural data and results from the model are compared with frequestist approach and with Bayesian model of uniform prior in terms of average relative bias, average squared relative bias and average absolute bias. The numerical results obtained highlighted the superiority of using the proposed prior over the uniform prior. Thus Bayes estimators (with new prior) of small area means have good frequentist properties such as MSE and ARB as compared to other traditional methods viz., Direct, Synthetic and Composite estimators.