Results 1 to 10 of about 540 (127)
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
exaly +6 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 +13 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 +5 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 +5 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 ...
Zakariya Yahya Algamal, Mohammad Arashi
exaly +5 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 +5 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 +5 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 +5 more sources
Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation [PDF]
Journal of New Theory, 2022 The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical ...
Tuğba Söküt Açar
doaj +4 more sources
Jackknife Kibria-Lukman M-Estimator: Simulation and Application
Journal of Nigerian Society of Physical Sciences, 2022 The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions.
Segun L. Jegede +3 more
doaj +3 more sources