| dc.contributor.author | Yuliani Setia Dewi | |
| dc.date.accessioned | 2014-04-10T04:26:56Z | |
| dc.date.available | 2014-04-10T04:26:56Z | |
| dc.date.issued | 2014-04-10 | |
| dc.identifier.uri | http://repository.unej.ac.id/handle/123456789/56811 | |
| dc.description.abstract | Correlation between predictor variables (multicollinearity) become a problem
in regression analysis. There are some methods to solve the problem. Each
method has complexity. One of those methods is PLS. This research aims to
describe the procedur of Estimation of PLS regression. PLS combine principal
component analysis and multiple linear regression. PLS method estimates
regression coefficient by selecting the number of component that is used in the
model that has optimum Mean Square Error (minimum MSE). Optimal MSE
is taken from cross validation. | en_US |
| dc.language.iso | other | en_US |
| dc.relation.ispartofseries | Majalah Ilmiah Matematika dan Statistika;Volume 9, Juni 2009 | |
| dc.subject | PLS, multicollinearity, MSE, cross validation | en_US |
| dc.title | PENDUGAAN KOEFISIEN REGRESI UNTUK DATA MENGANDUNG MULTIKOLINEARITAS MENGGUNAKAN PLS | en_US |
| dc.type | Article | en_US |
