期刊名称：Pakistan Journal of Statistics and Operation Research
印刷版ISSN：2220-5810
出版年度：2016
卷号：12
期号：3
页码：491-506
DOI：10.18187/pjsor.v12i3.1308
语种：English
出版社：College of Statistical and Actuarial Sciences
摘要：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.
其他摘要：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.
关键词：Small Area Estimation; Unit Level Model; Hierarchical Bayes
