Results 11 to 20 of about 197,245 (124)

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   +4 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   +3 more sources

New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application [PDF]

Journal of Applied Statistics
This 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, Solmaz Seifollahi   +2 more
exaly   +4 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   +2 more sources

A New Ridge‐Type Estimator for the Gamma Regression Model [PDF]

Scientifica, Volume 2021, Issue 1, 2021., 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.
Adewale F. Lukman, Issam Dawoud, B. M. Golam Kibria, Zakariya Y. Algamal, Benedicta Aladeitan, Francisco Ayuga   +5 more
wiley   +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   +4 more sources

A new hybrid estimator for linear regression model analysis: Computations and simulations

Scientific African, 2023
The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj   +2 more sources

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

Axioms, 2022
The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud, Fuad A. Awwad, Elsayed Tag Eldin, Mohamed R. Abonazel   +3 more
doaj   +2 more sources

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

The Scientific World Journal, Volume 2020, Issue 1, 2020., 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‐
Adewale F. Lukman, Kayode Ayinde, B. M. Golam Kibria, Segun L. Jegede, Roberto Rivelino   +4 more
wiley   +2 more sources

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

Scientifica, Volume 2020, Issue 1, 2020., 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.
B. M. Golam Kibria, Adewale F. Lukman, Osman Kucuk   +2 more
wiley   +2 more sources