Confidence intervals based on resampling methods using ridge estimator in linear regression model
In multiple regression analysis, the use of ridge regression estimator over the conventional ordinary least squares estimator was suggested by Hoerl and Kennard in 1970 to beat the problem of multicollinearity that may exist among the independent variables. Keeping this in mind, in the present study...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BİSKA Bilisim Company
2018-11-01
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| Series: | New Trends in Mathematical Sciences |
| Subjects: | |
| Online Access: | https://ntmsci.com/ajaxtool/GetArticleByPublishedArticleId?PublishedArticleId=8477 |
| Summary: | In multiple regression analysis, the use of ridge regression estimator over the conventional ordinary least squares estimator was suggested by Hoerl and Kennard in 1970 to beat the problem of multicollinearity that may exist among the independent variables. Keeping this in mind, in the present study, the authors intend to develop and compare different confidence intervals for regression coefficients based on ridge regression estimator using bootstrap and jackknife methodology. For comparison, the coverage probabilities and confidence widths are calculated through a simulation study for the data which suffers from the problem of multicollinearity |
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| ISSN: | 2147-5520 2147-5520 |