• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.229-244
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• 2007

Among the small area estimation methods, it has been known that hierarchical Bayesian(HB) approach is the most reasonable and effective method. However any model based approaches need good explanatory variables and finding them is the key role in the model based approach. As the lacking of explanatory variables, adopting the spatial terms in the model was introduced. Here in this paper, we evaluate the model based methods with/without spatial terms using the diagnostic methods which were introduced by Brown et al. (2001). And Economic Active Population Survey(2005) is used for data analysis.

• Journal of applied mathematics & informatics
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• v.3 no.1
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• pp.55-64
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• 1996

Let $X_1, ....$X_P be p($\geq$2) independent random variables, where each X1 has a gamma distribution with $k_i and ${\heta}_i$. The problem is to simultaneously estimate p gammar parameters ${\heta}_i$ under entropy loss where the parameters are believed priori. Hierarchical bayes(HB) and empirical bayes(EB) estimators are investigated. Next computer simulation is studied to compute the risk percentage improvement of the HB, EB and the estimator of Dey et al.(1987) compared to MVUE of ${\heta}$.