Results 101 to 110 of about 221 (137)

Chemometrics validation of adsorption process economy: Case study of acetaminophen removal onto quail eggshell adsorbents [PDF]

ADEKOLA, F.A, Amone, Kelvin, Bankole, Deborah, Bello, O.S., INYINBOR, A.A, Lukman, A. F., Oreofe, Toyin Adedayo   +6 more
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Surface functionalized plant residue in Cu2+ scavenging: Chemometrics of operational parameters for process economy validation [PDF]

ADEKOLA, F.A, Bankole, Deborah, Bello, O.S., INYINBOR, A.A, Lukman, A. F., Olatunji, Gabriel Ademola, Oreofe, Toyin Adedayo   +6 more
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A New biased estimator and variations based on the Kibria Lukman Estimator

Istanbul Journal of Mathematics, 2023
Summary: One of the problems encountered in linear regression models is called multicollinearity problem which is an approximately linear relationship between the explanatory variables. This problem causes the estimated parameter values to be highly sensitive to small changes in the data.
AKAY, Kadri Ulaş, ERTAN, Esra, ERKOÇ, Ali   +2 more
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Kibria‐Lukmantype estimator for gamma regression model

Concurrency and Computation: Practice and Experience, 2022
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, Fidelis Ifeayi Ugwuowo   +1 more
openaire   +1 more source

Modified jackknife Kibria–Lukman estimator for the Poisson regression model

Concurrency and Computation: Practice and Experience, 2021
AbstractPoisson 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, Fidelis Ifeanyi Ugwuowo   +1 more
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A robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers

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, Shakeel Ahmad, Muhammad Aslam, Muhammad Kashif   +3 more
openaire   +1 more source

New Class of Kibria–Lukman Estimator for Addressing Multicollinearity in Poisson Regression Model

Chiang Mai Journal of Science
Count 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, Ali T. Hammad, M.M. Abd El-Raouf, Ahmed M. Gemeay   +3 more
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Jackknife Kibria-Lukman estimator for the beta regression model

Communications in Statistics - Theory and Methods, 2023
Tuba Koç, Emre Dünder
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On the jackknife Kibria-Lukman estimator for the linear regression model

Communications in Statistics - Simulation and Computation, 2021
Fidelis Ifeanyi Ugwuowo, Henrietta Ebele Oranye, Kingsley Chinedu Arum   +2 more
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Generalized kibria-lukman estimator for multicollinearity in linear regression models: theoretical insights and comparative analysis

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, null Oladapo D.I, null Odeyemi A.S, null Obadina G.O   +3 more
openaire   +1 more source