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dc.contributor.authorMurtinasari, Frida
dc.contributor.authorHadi, Alfian Futuhul
dc.contributor.authorAnggraeni, Dian
dc.date.accessioned2017-09-26T08:20:58Z
dc.date.available2017-09-26T08:20:58Z
dc.date.issued2017-09-26
dc.identifier.issn1411-5735
dc.identifier.urihttp://repository.unej.ac.id/handle/123456789/81861
dc.descriptionJurnal ILMU DASAR Vol. 18 No. 1, Januari 2017 : 1 - 8en_US
dc.description.abstractSAE (Small Area Estimation) is often used by researchers, especially statisticians to estimate parameters of a subpopulation which has a small sample size. Empirical Best Linear Unbiased Prediction (EBLUP) is one of the indirect estimation methods in Small Area Estimation. The presence of outliers in the data can not guarantee that these methods yield precise predictions . Robust regression is one approach that is used in the model Small Area Estimation. Robust approach in estimating such a small area known as the Robust Small Area Estimation. Robust Small Area Estimation divided into several approaches. It calls Maximum Likelihood and MEstimation. From the result, Robust Small Area Estimation with M-Estimation has the smallest RMSE than others. The value is 1473.7 (with outliers) and 1279.6 (without outlier). In addition the research also indicated that REBLUP with M-Estimation more robust to outliers. It causes the RMSE value with EBLUP has five times to be large with only one outlier are included in the data analysis. As for the REBLUP method is relatively more stable RMSE results.en_US
dc.language.isoiden_US
dc.subjectSAEen_US
dc.subjectEBLUPen_US
dc.subjectRobusten_US
dc.subjectMaximum Likelihooden_US
dc.subjectM-Estimationen_US
dc.titleKebutuhan Rumah Sederhana di Kabupaten Jember dengan Robust Small Area Estimation (Simple House Needs in Jember with Robust Small Area Estimation)en_US
dc.typeArticleen_US


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