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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.
Apr 1, 2022 · 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...
The Kibria-Lukman estimator (KLE) was suggested as an alternative to the OLSE and some other estimators (ridge and Liu estimators). In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE).
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.
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What is the Kibria-Lukman estimator?
Can a Kibria-Lukman estimator solve a multicollinearity problem?
What is a jackknife Kibria-Lukman estimator?
Sep 20, 2024 · Following the works of [15,16], Kibria and Lukman developed the Kibria–Lukman estimator (KLE), a single-parameter biased estimator designed to address multicollinearity in linear regression models. They demonstrated that this estimator outperforms both the Ridge and Liu estimators in terms of estimation accuracy and stability.
