Results 21 to 30 of about 173 (111)

Performance of the Ridge and Liu Estimators in the zero‐inflated Bell Regression Model

Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022
The Poisson regression model is popularly used to model count data. However, the model suffers drawbacks when there is overdispersion—when the mean of the Poisson distribution is not the same as the variance. In this situation, the Bell regression model fits well to the data. Also, there is a high tendency of excess zeros in the count data.
Zakariya Yahya Algamal, Adewale F. Lukman, Mohamed R. Abonazel, Fuad A. Awwad, Niansheng Tang   +4 more
wiley   +1 more source

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, Mohamed R. Abonazel, Fuad A. Awwad   +2 more
doaj   +1 more source

Kibria–Lukman-Type Estimator for Regularization and Variable Selection with Application to Cancer Data

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 Folaranmi Lukman, Jeza Allohibi, Segun Light Jegede, Emmanuel Taiwo Adewuyi, Segun Oke, Abdulmajeed Atiah Alharbi   +5 more
doaj   +1 more source

Robust-M new two-parameter estimator for linear regression models: Simulations and applications [PDF]

, 2023
In the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously.
A. A. Akomolafe, A. S. Ajiboye, K. Ayinde, O. S. Makinde, Taiwo Joel Adejumo   +4 more
core   +2 more sources

Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation

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 ...
K.C. Arum, F.I. Ugwuowo, H.E. Oranye, T.O. Alakija, T.E. Ugah, O.C. Asogwa   +5 more
doaj   +1 more source

The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application

Journal of Nigerian Society of Physical Sciences, 2023
This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the ...
Oluwayemisi Oyeronke Alaba, B. M. Golam Kibria   +1 more
doaj   +1 more source

Efficient estimation and validation of shrinkage estimators in big data analytics [PDF]

, 2023
DATA AVAILABILITY STATEMENT: Data is available from the authors on request.Shrinkage estimators are often used to mitigate the consequences of multicollinearity in linear regression models.
Arashi, Mohammad, Du Plessis, Salomi, Maribe, Gaonyalelwe, Millard, Salomon M.   +3 more
core   +1 more source

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, Ashrakat Adel Saber, Fuad A. Awwad   +2 more
doaj   +1 more source

On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model [PDF]

, 2022
The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity problem,
Abiola Timothy Owolabi, Janet Iyabo Idowu, Kayode Ayinde, Olasunkanmi James Oladapo   +3 more
core   +2 more sources

Restricted ride estimator in the Inverse Gaussian regression model [PDF]

, 2022
The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. Its parameters are usually estimated using maximum likelihood (ML) method.
Algamal, Zakariya Yahya, Alsarraf, Israa
core   +5 more sources