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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.
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 · To eliminate the adverse effects of multicollinearity in the negative binomial regression model, we propose the use of a jackknife version of the Kibria–Lukman estimator.
May 29, 2022 · This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers.
This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers. We examine the superiority of the estimator over existing estimators using theoretical proofs and Monte Carlo simulations.
- Kayode Ayinde
The main objective given in this paper is to use Jackknife approach with the new ridge-type estimator (K-L estimator) of Kibria and Lukman (2020). Our proposed estimator will efficiently help to decrease the biasness of K-L estimator in Bell regression model. The superiority of our
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What is the Kibria-Lukman estimator?
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Can a Kibria-Lukman estimator solve a 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...
