A generalized diagonal ridge-type estimator in linear regression. (English) Zbl 1462.62445
Summary: This article introduces a general class of biased estimator, namely a generalized diagonal ridge-type (GDR) estimator, for the linear regression model when multicollinearity occurs. The estimator represents different kinds of biased estimators when different parameters are obtained. Some properties of this estimator are discussed and an iterative procedure is provided for selecting the parameters. A Monte Carlo simulation study and an application show that the GDR estimator performs much better than the ordinary least squares (OLS) estimator under the mean square error (MSE) criterion when severe multicollinearity is present.
MSC:
| 62J07 | Ridge regression; shrinkage estimators (Lasso) |
Keywords:
admissibility; consistency; generalized diagonal ridge-type estimator; linear regression; mean square errorReferences:
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