Results 21 to 30 of about 133 (94)

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, Adewale F. Lukman, Kayode Ayinde, Kehinde A. Odeniyi   +3 more
doaj   +2 more sources

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. [PDF]

Scientifica (Cairo), 2020
The ridge regression‐type (Hoerl and Kennard, 1970) and Liu‐type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model.
Kibria BMG, Lukman AF.
europepmc   +2 more sources

Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model. [PDF]

ScientificWorldJournal, 2020
The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two‐
Lukman AF, Ayinde K, Golam Kibria BM, Jegede SL.   +3 more
europepmc   +2 more sources

K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model

Mathematics, 2023
Multicollinearity negatively affects the efficiency of the maximum likelihood estimator (MLE) in both the linear and generalized linear models. The Kibria and Lukman estimator (KLE) was developed as an alternative to the MLE to handle multicollinearity ...
Adewale F. Lukman, B. M. Golam Kibria, Cosmas K. Nziku, Muhammad Amin, Emmanuel T. Adewuyi, Rasha Farghali   +5 more
doaj   +5 more sources

New two parameter hybrid estimator for zero inflated negative binomial regression models [PDF]

Scientific Reports
The zero-inflated negative binomial regression (ZINBR) model is used for modeling count data that exhibit both overdispersion and zero-inflated counts. However, a persistent challenge in the efficient estimation of parameters within ZINBR models is the ...
Fatimah A. Almulhim, M. Nagy, Ali T. Hammad, A. H. Mansi, Getachew Tekle Mekiso, M. M. Abd El-Raouf   +5 more
doaj   +2 more sources

A New Kibria-Lukman-Type Estimator for Poisson Regression Models

Acta Infologica
One of the most important models for the analysis of count data is the Poisson Regression Model (PRM). The parameter estimates of the PRM are obtained by the Maximum Likelihood Estimator (MLE).
Cemal Çiçek, Kadri Ulaş Akay
doaj   +3 more sources

An Alternative Estimator for Poisson–Inverse-Gaussian Regression: The Modified Kibria–Lukman Estimator

Algorithms
Poisson regression is used to model count response variables. The method has a strict assumption that the mean and variance of the response variable are equal, while, in practice, the case of overdispersion is common.
Rasha A. Farghali, Adewale F. Lukman, Zakariya Algamal, Murat Genc, Hend Attia   +4 more
doaj   +2 more sources

Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator

Axioms
Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco–Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the ...
Adewale F. Lukman, Suleiman Mohammed, Olalekan Olaluwoye, Rasha A. Farghali   +3 more
doaj   +2 more sources

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

Kuwait Journal of Science
The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has
Danish Wasim, Muhammad Suhail, Sajjad Ahmad Khan, Maha Shabbir, Fuad A. Awwad, Emad A.A. Ismail, Hijaz Ahmad, Amjad Ali   +7 more
doaj   +2 more sources

Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

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   +1 more source