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MODEL REGRESI LINIER BERGANDA MENGGUNAKAN PENAKSIR PARAMETER REGRESI ROBUST M-ESTIMATOR (Studi Kasus: Produksi Padi di Provinsi Jawa Barat Tahun 2009)

JURNAL ISTEK · Vol. 6 No. 1 (2015)

Terbit
2015-08-01
Penerbit
JURNAL ISTEK
Bahasa
eng

Abstrak

The least squares method is one method of parameter estimation in regression models. This method produces an unbiased estimator with consideration the assumptions are linearity, non-multicollinearity, non-autocorrelation, homoscedastic, and normally distributed error fulfilled. When the assumptions are not fulfilled, such as error distribution is not normal due to the existence of outliers, an estimation obtained are not exact.An alternative method that can overcome the problem of outliers is robust regression method using M-estimator (Iteratively Reweighted Least Squares). M-estimator is an iterative method using weighting function Huber and Tukey bisquare to estimate the parameters or coefficients in the regression model. The best model obtained by M-estimator robust method using Huber and Tukey bisquare is determined by the value R2adjusted and standard error values.

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.uinsgd.ac.id/index.php/istek/oai, pertama di-harvest 7 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">MODEL REGRESI LINIER BERGANDA MENGGUNAKAN PENAKSIR PARAMETER REGRESI  ROBUST  M-ESTIMATOR (Studi Kasus: Produksi Padi di Provinsi Jawa Barat Tahun 2009)</dc:title>
	<dc:creator>Cahyandari, Rini</dc:creator>
	<dc:creator>Hisani, Nurul</dc:creator>
	<dc:description xml:lang="en-US">The least squares method is one method of parameter estimation in regression models.                 This method produces an unbiased estimator with consideration the assumptions are linearity,             non-multicollinearity, non-autocorrelation, homoscedastic, and normally distributed error fulfilled. When the assumptions are not fulfilled, such as error distribution is not normal due to the existence of outliers, an estimation obtained are not exact.An alternative method that can overcome the problem of outliers is robust regression method using M-estimator (Iteratively Reweighted Least Squares). M-estimator is an iterative method using weighting function Huber and Tukey bisquare  to estimate the parameters or coefficients in the regression model. The best model obtained by M-estimator robust method using Huber and Tukey bisquare is determined by the value R2adjusted and standard error values.</dc:description>
	<dc:publisher xml:lang="en-US">JURNAL ISTEK</dc:publisher>
	<dc:date>2015-08-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.uinsgd.ac.id/index.php/istek/article/view/291</dc:identifier>
	<dc:source xml:lang="en-US">JURNAL ISTEK; Vol. 6 No. 1-2 (2012): ISTEK</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.uinsgd.ac.id/index.php/istek/article/view/291/305</dc:relation>
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