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  <title>Estimasi Varians Dengan Pendekatan Metode Rescaled Bootstrap (Studi Kasus :</title>
  <subTitle>Rata - Rata Lama Sekolah Provinsi Sulawesi Barat)</subTitle>
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 <name type="Personal Name" authority="">
  <namePart>Sapta Hastho Ponco</namePart>
  <role>
   <roleTerm type="text">Primary Author</roleTerm>
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  <place>
   <placeTerm type="text">Bandung</placeTerm>
   <publisher>Magister Statistika Terapan</publisher>
   <dateIssued>2015</dateIssued>
  </place>
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  <languageTerm type="code">id</languageTerm>
  <languageTerm type="text">Indonesia</languageTerm>
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 <note>Untuk meningkatkan kualitas data survei, Badan Pusat Statistik (BPS) &#13;
melakukan kegiatan pengembangan Survei Sosial Ekonomi Nasional (Susenas) &#13;
dengan kajian utama pada kecukupan sampel rumah tangga sebagai unit sampling. &#13;
Salah satu kendala yang ditemui pada kajian tersebut adalah dibutuhkannya &#13;
anggaran yang besar. Dalam keadaan demikian penggunaan boots trap dapat &#13;
disarankan. Namun jika metode bootstrap diterapkan pada desain survei yang &#13;
berstrata, estimasi varians yang dihasilkan menjadi bias dan sampel memberikan &#13;
basil estimasi yang tidak konsisten. Oleh karena itu, untuk mengatasinya &#13;
digunakan metode rescaled boots trap. &#13;
&#13;
Metode rescaled boots trap diterapkan pada penghitungan estimasi Rata­ &#13;
Rata Lama Sekolah dengan cakupan level kabupatenikota dan provinsi &#13;
berdasarkan data Susenas tahun 2012 di Provinsi Sulawesi Barat. Bobot bootstrap &#13;
diberikan pada seluruh sampel rumah tangga terpilih pada replikasi ke-I 000. Hasil &#13;
estimasi menunjukkan standard error MYS dengan 9 sampel rumah tangga setiap &#13;
blok sensus bemilai lebih rendah dibanding standard error Susenas tahun 2012. &#13;
&#13;
4. Abstract &#13;
&#13;
In order to improve the quality of the survey data, Statistics Indonesia &#13;
(BPS) developing perform activities of the National Socio-Economic Survey &#13;
(Susenas) with a major study on the sufficiency sample households as a unit of &#13;
sampling. One of the obstacles encountered is needed a large budget. In these &#13;
circumstances, the use ofbootstrap method is recommended. Nevertheless, the use &#13;
of bootstrap method is not fully applicable to the complex survey design involving &#13;
stratification. If it remains enforced, the variance estimates will be biased and the &#13;
sample will yield inconsistent estimates. To overcome such problems, the rescaled &#13;
'bootstrap method can be applied. &#13;
&#13;
The rescaled bootstrap method is applied to the calculation of estimates &#13;
Mean Years of Schooling (MYS) with coverage at the regency and provinces &#13;
based on Susenas 2012 in the province of West Sulawesi. Bootstrap weights given &#13;
to the whole sample of households selected at 1000th replicate. The results of &#13;
estimation indicate standard error of the MYS which consists of 9 sample of &#13;
households is lower than the standard error of Susenas 2012. &#13;
&#13;
</note>
 <note type="statement of responsibility">Sapta Hastho Ponco</note>
 <subject authority="">
  <topic>Estimasi Varians Dengan Pendekatan Metode  Rescale</topic>
 </subject>
 <classification>519.5</classification>
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