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Kibria–Lukman (KL) estimator
- As an alternative to the ridge and Liu estimators, Kibria and Lukman proposed new ridge–type estimator to resolve the issue of multicollinearity in the linear regression model. This estimator is called the Kibria–Lukman (KL) estimator.
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The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model.
Mar 1, 2023 · As an alternative to the ridge and Liu estimators, Kibria and Lukman [16] proposed new ridge–type estimator to resolve the issue of multicollinearity in the linear regression model. This estimator is called the Kibria–Lukman (KL) estimator.
Kibria and Lukman proposed a new estimator called the ridge-type estimator and applied to the popular linear regression model. The main objective portrayed in this article is to extend the new ridge-type estimator of Kibria and Lukman [ 17 ] to the GRM.
The Kibria-Lukman (KL) estimator is a recent estimator that has been proposed to solve the multicollinearity problem. In this paper, a generalized version of the KL estimator is proposed, along with the optimal biasing parameter of our proposed estimator derived by minimizing the scalar mean squared error.
Recently, Kibria and Lukman (2020) developed the KL estimator and found it preferable to the ridge estimator. In this study, we modified the KL estimator to propose a new estimator. The new estimator is called the Modified KL estimator.
Nov 22, 2022 · To circumvent the problem of multicollinearity in regression models, a ridge-type estimator is recently proposed in the literature, which is named as the Kibria–Lukman estimator (KLE). The KLE has better properties than the conventional ridge regression estimator.
Sep 20, 2024 · Ridge and Kibria–Lukman estimators have been applied to address multicollinearity, but their performance often deteriorates in the presence of outliers. Robust estimation techniques have been proposed to handle the impact of outliers in NBR.
