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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.
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.
INTRODUCTION. The statistical consequences of multicollinearity are well-known in statistics for a linear regression model. Multicollinearity is known as the approximately linear dependency among the columns of the matrix X in the following linear model y = Xβ +ε,ε ∼ N. 0,σ2In.
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.
Jan 9, 2023 · Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity for the linear regression model.
Dec 14, 2021 · This study introduces a new biased estimator called the K‐L estimator for the linear mixed model to overcome the effect of multicollinearity. We derived the mean squared error property of the proposed estimator and made a theoretical comparison with other methods.
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Can Kibria-Lukman estimator solve multicollinearity problem?
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Jul 20, 2022 · Ozbay and Kaciranlar 20 integrated two parameter estimator and mixed estimator and proposed a two parameter mixed estimator. In this paper, a new mixed KL estimator under stochastic restrictions is proposed, and its excellent properties under certain conditions are proved theoretically.
