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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.
May 29, 2022 · This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers.
Jackknife. One of the earliest techniques to obtain reliable statistical estimators is the jackknife technique. It requires less computational power than more recent techniques. Suppose we have a sample x ( x , x ,..., 2. x ) and an estimator.
- 169KB
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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
In 2020, Kibria and Lukman proposed a new ridge-type estimator for the linear regression model. This proposed estimator is called as Kibria-Lukman (KL) estimator, which is defined as (B. M. Golam Kibria & Lukman, 2020):
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Dec 14, 2021 · Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the Kibria-Lukman (KL) estimator, though literature shows that the KL estimator is preferred. Therefore, this study sought to modify the KL estimator to mitigate the Poisson Regression Model with multicollinearity.
