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dc.contributor.authorSadik, Kusman
dc.date.accessioned2013-01-15T07:56:53Z
dc.date.available2013-01-15T07:56:53Z
dc.date.issued2009
dc.identifier.issn0853-8115
dc.identifier.urihttp://repository.ipb.ac.id/handle/123456789/59468
dc.description.abstractThere have been two main topics developed by statisticians in a survey, i.e. sampling techniques and estimation methods. The current issues in estimation methods related to estimation of a particular domain having small size of samples or, in more extreme cases, there is no sample available for direct estimation. Sample survey data provide effective reliable estimators of totals and means for large area and domains. But it is recognized that the usual direct survey estimator performing statistics for a small area, have unacceptably large standard errors, due to the circumstance of small sample size in the area. The most commonly used models for this case, usually in small area estimation, are based on generalized linear mixed models. Some time happened that some surveys are carried out periodically so that the estimation could be improved by incorporating both the area and time random effects. In this paper we propose a state space model which accounts for the two random effects and is based on two equation, namely transition equation and measurement equation. Based on a evaluation criterion, the proposed hierarchical Bayes estimator turns out to be superior to both estimated best linear unbiased prediction (BLUP) and the direct survey estimator. The posterior variances which measure accuracy of the hierarchical Bayes estimates are always smaller than the corresponding variances of the BLUP and the direct survey estimates.en
dc.publisherIPB (Bogor Agricultural University)
dc.relation.ispartofseriesVol. 14 No. 2, 2009;
dc.subjectGeneralized liear mixed modelen
dc.subjecthierarchidal Bayesen
dc.subjectbest linear unbiased predictionen
dc.subjectprior and posterior functionen
dc.subjectgeneralized variance functionen
dc.subjectblock diagonal covarianceen
dc.subjectstate space modelen
dc.titleMetode Prediksi Tak-Bias Linear Terbaik Dan BayesBerhirarki Untuk Pendugaan Area Kecil Berdasarkan Model State Spaceen
dc.title.alternativeForum Statistika dan Komputasi, Vol 13 No.2, 2008en


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