New ridge parameters for ridge regression

By A. V. Dorugade. 2014

127 citations recorded by the source corpus.

Abstract Hoerl and Kennard (1970a) introduced the ridge regression estimator as an alternative to the ordinary least squares (OLS) estimator in the presence of multicollinearity. In ridge regression, ridge parameter plays an important role in parameter estimation. In this article, a new method for estimating ridge parameters in both situations of ordinary ridge regression (ORR) and generalized ridge regression (GRR) is proposed. The simulation study evaluates the performance of the proposed estimator based on the mean squared error (MSE) criterion and indicates that under certain conditions the proposed estimators perform well compared to OLS and other well-known estimators reviewed in this article.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/512aafc2cbca931603e65d7b331dc98e3eb4e5b3

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In Statistics and Probability

1. Emmanuel T. Adewuyi
2. Sek Siok Kun
3. Adewale F. Lukman
4. Kayode Ayinde
5. Issam Dawoud
6. B. M. G. Kibria

Citations in the corpus

• A Modified New Two-Parameter Estimator in a Linear Regression Model (2019, background): “-is includes Hoerl and Kennard [1], Kibria [11], Muniz and Kibria [12], Aslam [13], Dorugade [14], Kibria and Banik [15], Lukman and Ayinde [16], Lukman et al. [17], and others.”