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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.
Nov 26, 2021 · The Kibria-Lukman estimator (KLE) was suggested as an alternative to the OLSE and some other estimators (ridge and Liu estimators). In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE).
In this paper, a jackknifed version of the K-L estimator in the Bell regression model is proposed, which combines the Jackknife process with the K-L estimator to reduce biasedness.
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.
Nov 26, 2021 · In this study, we proposed a hybrid estimator by combining the Kibria-Lukman estimator with the modified ridge-type estimator. The proposed estimator theoretically dominates the existing...
The bias-corrected jackknife estimate of is b jack = b bias(d b) = n b (n 1) b Mathematically, it can be shown that bias(d b) is an unbiased estimator of the true bias for many statistics. For other statistics, although b jack is a biased estimator of the true bias, the bias of b jack is reduced in comparison to the unadjusted estimate b. 41
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.
