Results 101 to 110 of about 218 (135)
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SummaryThe gamma regression model explores the relationship between a skewed response variable and one or more independent variables. The method of maximum likelihood is popularly adopted to model the relationship. However, the method performance drops when linear dependency exists among the predictors (multicollinearity). In this article, we develop a
Gladys Amos Shewa +1 more
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Modified jackknife Kibria–Lukman estimator for the Poisson regression model
Concurrency and Computation: Practice and Experience, 2021AbstractPoisson regression is one of the methods to analyze count data and, the regression parameters are usually estimated using the maximum likelihood (ML) method. However, the ML method is sensitive to multicollinearity. Multicollinearity occurs when there is linear dependency among the explanatory variables.
Henrietta Ebele Oranye +1 more
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Concurrency and Computation: Practice and Experience, 2022
SummaryTo 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. However, the presence of outliers in the data set may have some adverse effects on
Abdul Majid +3 more
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SummaryTo 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. However, the presence of outliers in the data set may have some adverse effects on
Abdul Majid +3 more
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New Class of Kibria–Lukman Estimator for Addressing Multicollinearity in Poisson Regression Model
Chiang Mai Journal of ScienceCount data are prevalent across various disciplines, and the Poisson regression model (PRM) is often employed to analyze such data due to its widespread popularity. The model’s parameters are typically estimated using the maximum likelihood estimator (MLE). However, when multicollinearity exists among the explanatory variables, MLE may lead to unstable
Ohud A. Alqasem +3 more
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Jackknife Kibria-Lukman estimator for the beta regression model
Communications in Statistics - Theory and Methods, 2023Tuba Koç, Emre Dünder
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On the jackknife Kibria-Lukman estimator for the linear regression model
Communications in Statistics - Simulation and Computation, 2021Fidelis Ifeanyi Ugwuowo +2 more
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International Journal of Science and Technology Research Archive
Multicollinearity, a common issue in regression models caused by high correlations among explanatory variables, undermines the stability and reliability of traditional estimators like Ordinary Least Squares (OLS). This study investigates the Generalized Kibria-Lukman (GKL) estimator, introduced by Dawoud et al.
null Ayanlowo E.A +3 more
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Multicollinearity, a common issue in regression models caused by high correlations among explanatory variables, undermines the stability and reliability of traditional estimators like Ordinary Least Squares (OLS). This study investigates the Generalized Kibria-Lukman (GKL) estimator, introduced by Dawoud et al.
null Ayanlowo E.A +3 more
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Some Modified Kibria-Lukman Estimators for the Gamma Regression Model
التجارة والتمويل, 2022openaire +1 more source
Combination of the modified Kibria–Lukman and the principal component regression estimators
Communications in Statistics - Simulation and Computation, 2023Dan Huang, Jiewu Huang, Dewei Bai
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Communications in Statistics - Simulation and Computation, 2023
Muhammad Nauman Akram +3 more
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Muhammad Nauman Akram +3 more
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