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- 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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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.
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...
called the Kibria–Lukman (KL) estimator which is defined by βˆKL = (X′X +kIp)−1(X′X −kIp)βˆ,k > 0 This estimator has been extended for use in different generalized linear models, such as Lukman et al. [4, 5], Akram et al. [6], and Abonazel et al. [7]. According to recent papers [8–10], we can say that the efficiency of any ...
Dec 14, 2021 · MSE(βˆPLE)= ∑P j=1 (λj + d)2 λj(λj + 1)2 + (d − 1)2 ∑p j−1 α2j (λj + 1)2. (2.9) where λj is the j th eigenvalue of X′LˆX and α j is the j th element of α. The KL estimator was proposed by Kibria and Lukman (2020) as a means of mitigating the effect of multicollinearity on parameter estimation.
Apr 11, 2023 · Kibria and Lukman proposed the Almon-Kibria-Lukman (KL) estimator and found that the estimator dominates the Almon-ridge estimator. In this study, we proposed the Almon-PC-KL estimator by combining the Almon-PC estimator with the KL estimator to handle multicollinearity in the distributed lag model.
Nov 26, 2021 · In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE). We derived the statistical properties of the new estimator and compared it theoretically with the KLE and some other existing estimators.
