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1. www.frontiersin.org › journals › applied-mathematicsFrontiers | Generalized Kibria-Lukman Estimator: Method ...

   www.frontiersin.org › journals › applied-mathematics

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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.

2. www.sciencedirect.com › science › articleKibria–Lukman estimator for the Conway–Maxwell Poisson ...

   www.sciencedirect.com › science › article

   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. f1000research.com › articles › 10-548Modified Kibria-Lukman (MKL) estimator for the... | F1000Research

   f1000research.com › articles › 10-548

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   Jul 8, 2021 · In this study, we propose the Modified Kibria-Lukman estimator to handle multicollinearity in PRM. The estimator is a single parameter estimator which makes it less computationally intensive as compared with the two-parameter estimators.

   • Author: Benedicta B. Aladeitan, Olukayode Adebimpe, Adewale F. Lukman, Olajumoke Oludoun, Oluwakemi E. Abiod...

   • Publish Year: 2021

4. www.researchgate.net › publication › 360095086(PDF) Generalized Kibria-Lukman Estimator: Method, Simulation ...

   www.researchgate.net › publication › 360095086

   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...

5. www.tandfonline.com › doi › fullOn the jackknife Kibria-Lukman estimator for the linear ...

   www.tandfonline.com › doi › full

   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. www.semanticscholar.org › paper › Generalized-KibriaGeneralized Kibria-Lukman Estimator: Method, Simulation, and ...

   www.semanticscholar.org › paper › Generalized-Kibria

   Apr 20, 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 with the optimal biasing parameter of our proposed estimator derived by minimizing the scalar mean squared error.

7. People also ask

   What is the Kibria-Lukman 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.

   (PDF) Generalized Kibria-Lukman Estimator: Method, Simulation, and

   www.researchgate.net/publication/360095086_Generalized_Kibria-Lukman_Estimator_Method_Simulation_and_Application

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   What is 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.

   Kibria–Lukman estimator for the Conway–Maxwell Poisson regression m…

   www.sciencedirect.com/science/article/pii/S2468227623000121

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   Does Kibria-Lukman estimator reduce multicollinearity?

   • 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. We demonstrated that our proposed estimator is more efficient than previous biased estimators proposed in the literature (ridge and Liu estimators).

   Kibria–Lukman estimator for the Conway–Maxwell Poisson regression m…

   www.sciencedirect.com/science/article/pii/S2468227623000121

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   What is a jackknife Kibria-Lukman estimator?

   • On the jackknife Kibria-Lukman estimator .... The linear regression model explores the relationship between the dependent variable and the independent variables. The ordinary least squared estimator (OLSE) is widely applicable to estimate the parameters of the model.

   On the jackknife Kibria-Lukman estimator for the linear regression

   www.tandfonline.com/doi/full/10.1080/03610918.2021.2007401

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   How is the performance of the proposed estimator compared with OLS?

   • Theoretically, the performance of the proposed estimator is compared with the OLS, the generalized ridge, the generalized Liu, and the KL estimators by the matrix mean squared error. Furthermore, a simulation study and the numerical example were performed for comparing the performance of the proposed estimator with the OLS and the KL estimators.

   (PDF) Generalized Kibria-Lukman Estimator: Method, Simulation, and

   www.researchgate.net/publication/360095086_Generalized_Kibria-Lukman_Estimator_Method_Simulation_and_Application

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   Is the KLE based on a M-estimator robust?

   • The KLE has better properties than the conventional ridge regression estimator. However, the presence of outliers in the data set may have some adverse effects on the KLE. To address this issue, the present article proposes a robust version of the KLE based on the M-estimator.

   A robust Kibria–Lukman estimator for linear regression model to combat

   onlinelibrary.wiley.com/doi/10.1002/cpe.7533

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8. onlinelibrary.wiley.com › doi › 10A robust Kibria–Lukman estimator for linear regression model ...

   onlinelibrary.wiley.com › doi › 10

   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.