REGRESI RIDGE DENGAN BENTUK Q DAN FAKTOR SHRINKAGE MENGGUNAKAN SINGULAR VALUE DECOMPOSITION
One of the assumptions in a multiple linear regression is no multicollinearity or no linear relationship between the independent variables. Multicollinearity causes the MSE of the least squares estimators become so great and the estimator obtained from the least squares method is not appropriate. In...
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| Main Authors: | , |
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| Format: | Theses and Dissertations NonPeerReviewed |
| Published: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/131263/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=71715 |
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| Summary: | One of the assumptions in a multiple linear regression is no multicollinearity
or no linear relationship between the independent variables. Multicollinearity causes
the MSE of the least squares estimators become so great and the estimator obtained
from the least squares method is not appropriate. In fact, the study is expected in a
model that has minimum variance, though biased.
Ridge regression with Q-shape and shrinkage factors is one way to solve the
problem of multicollinearity because it produces small MSE despite the relatively
small bias. This method reduced the coefficient of linear regression using shrinkage
factors to shrink the coefifisien and controlling them using the Q-shape. This method
is a modification of the ordinary ridge regression method using singular value
decomposition by change the component of independent variable without changing
the characteristics of the independent variables. |
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