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  1. 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.

  2. Mar 1, 2023 · In this paper, we proposed an extended version of the Kibria–Lukman estimator (COMPKL estimator) to the Conway–Maxwell Poisson regression model to reduce the effect of the multicollinearity problem.

  3. 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...

  4. 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.

  5. 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.

  6. In the linear regression model, the multicollinearity effects on the ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) estimator is a recent estimator that has

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  8. 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.

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