Results 11 to 20 of about 133 (94)
On the mixed Kibria–Lukman estimator for the linear regression model [PDF]
Scientific Reports, 2022 This paper considers a linear regression model with stochastic restrictions,we propose a new mixed Kibria–Lukman estimator by combining the mixed estimator and the Kibria–Lukman estimator.This new estimator is a general estimation, including OLS ...
Jibo Wu, Wu Jibo
exaly +5 more sources
Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 2; peer review: 2 approved, 1 approved with reservations] [PDF]
F1000Research, 2021 Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe +4 more
doaj +12 more sources
Generalized Kibria-Lukman Estimator: Method, Simulation, and Application
Frontiers in Applied Mathematics and Statistics, 2022 In the linear regression model, the multicollinearity effects on the ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) estimator is a recent estimator that has been
Issam Dawoud +2 more
exaly +4 more sources
International Journal of Mathematics and Mathematical SciencesThe Poisson regression model (PRM) is a widely used statistical technique for analyzing count data. However, when explanatory variables in the model are correlated, the estimation of regression coefficients using the maximum likelihood estimator (MLE ...
Hleil Alrweili
doaj +4 more sources
Kibria–Lukman estimator for the Conway–Maxwell Poisson regression model: Simulation and applications
Scientific African, 2023 The Conway–Maxwell Poisson (COMP) regression model is one of the count data models to account for over– and under–dispersion. In regression analysis, when the explanatory variables are correlated, when there is multicollinearity problem, this inflates ...
Mohamed R Abonazel, Fuad A Awwad
exaly +4 more sources
New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application [PDF]
Journal of Applied StatisticsThis paper examines the Cox proportional hazards model (CPHM) in the presence of multicollinearity. Typically, the maximum partial likelihood estimator (MPLE) is employed to estimate the model coefficients, which works well when the covariates are uncorrelated.
Zakariya Yahya Algamal, Mohammad Arashi
exaly +4 more sources
Scientific African, 2023 Scholars usually adopt the method of least squared to model the relationship between a response variable and two or more explanatory variables. Ordinary least squares estimator's performance is good when there is no outliers and multicollinearity in the ...
Kingsley Chinedu Arum
exaly +4 more sources
Mathematics, 2023 Following the idea presented with regard to the elastic-net and Liu-LASSO estimators, we proposed a new penalized estimator based on the Kibria–Lukman estimator with L1-norms to perform both regularization and variable selection.
Adewale F Lukman +2 more
exaly +4 more sources
MathematicsThis study presents a novel estimator that combines the Kibria–Lukman and ridge estimators to address the challenges of multicollinearity in Conway–Maxwell–Poisson (COMP) regression models.
Adewale F Lukman +2 more
exaly +4 more sources
A New Ridge-Type Estimator for the Gamma Regression Model. [PDF]
Scientifica (Cairo), 2021 The known linear regression model (LRM) is used mostly for modelling the QSAR relationship between the response variable (biological activity) and one or more physiochemical or structural properties which serve as the explanatory variables mainly when the distribution of the response variable is normal.
Lukman AF +4 more
europepmc +2 more sources