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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.
Mar 1, 2023 · To examine the presence of multicollinearity, we use three methods: the coefficient of the correlation between x's, the variance inflation factor (VIF), and the condition number (CN). The correlation coefficients are ρ X 1, X 2 = 0.97, ρ X 1, X 3 = 0.98, and ρ X 2, X 3 = 0.94, the values of the VIF are 54.36, 16.76, and 28.75, and the CN ...
The statistical consequences of multicollinearity are well-known in statistics for a linear regression model. Multicollinearity is known as the approximately linear dependency among the columns...
Apr 1, 2022 · 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 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.
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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Sep 20, 2024 · Cov(𝛽̂𝑁𝐵𝑅𝑅𝐸) =(𝑋′𝑊̂𝑋 + 𝑘𝐼)−1𝑋′𝑊̂𝑋(𝑋′𝑊̂𝑋 + 𝑘𝐼)−1. (11) Following the works of [15, 16], Kibria and Lukman [18] developed the Kibria–Lukman estimator (KLE), a single-parameter biased estimator designed to address multicollinearity in linear regression models.
