Estimation of Parameters for Small Areas Using Hierarchical Bayes Method in the Case of Known Model Hyperparameters
Estimation of Parameters for Small Areas Using Hierarchical Bayes Method in the Case of Known Model Hyperparameters

Summary/Abstract: In the paper the method of parameters estimation using hierarchical Bayes (HB) method in the case of known model hyperparameters for a priori conditionals was presented. This approach has some advantage in comparison with subjective model parameters selection because of more simulation stability and allows obtaining estimates that has more regular distribution. As an example the data about average per capita income from Polish Household Budget Survey for counties (NUTS4) and auxiliary variables from Polish Tax Register (POLTAX) were used. The computation was done using WinBUGS software and R-project environment with R2WinBUGS package, which control the simulations in WinBUGS, and coda package, which allows performing the analysis of simulation results. In the paper sample code in R-project that can be used as a pattern for further similar applications was also presented. The efficiency of hierarchical Bayes estimation with other small area methods was compared. Such comparison was done for HB and EBLUP techniques, for which some consistency related to the precision of estimates obtained using both techniques was achieved.

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