Results 41 to 50 of about 133 (94)

Modified One‐Parameter Liu Estimator for the Linear Regression Model

Modelling and Simulation in Engineering, Volume 2020, Issue 1, 2020., 2020
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter.
Adewale F. Lukman, B. M. Golam Kibria, Kayode Ayinde, Segun L. Jegede, Mohamed B. Trabia   +4 more
wiley   +1 more source

A Modified Two Parameter Estimator with Different Forms of Biasing Parameters in the Linear Regression Model

African Scientific Reports, 2022
Despite its common usage in estimating the linear regression model parameters, the ordinary least squares estimator often suffers a breakdown when two or more predictor variables are strongly correlated.
Abiola T. Owolabi, Kayode Ayinde, Olusegun O. Alabi   +2 more
doaj   +1 more source

Dawoud–Kibria Estimator for Beta Regression Model: Simulation and Application

Frontiers in Applied Mathematics and Statistics, 2022
The linear regression model becomes unsuitable when the response variable is expressed as percentages, proportions, and rates. The beta regression (BR) model is more appropriate for the variable of this form.
Mohamed R. Abonazel, Issam Dawoud, Fuad A. Awwad, Adewale F. Lukman   +3 more
doaj   +1 more source

Performance Evaluation of Measures of Central Tendency (MCT) in Tackling Missing Data in Collinear Regressors: A Comparison of Linear Estimators [PDF]

The Egyptian Statistical Journal
The presence of missing data and multicollinearity in regression analysis poses significant challenges to the reliability and accuracy of model estimates.
Abass Taiwo, Oluwatobi Ogunnusi, Timothy Olatayo, Biodun Efuwape   +3 more
doaj   +1 more source

A Modified New Two‐Parameter Estimator in a Linear Regression Model

Modelling and Simulation in Engineering, Volume 2019, Issue 1, 2019., 2019
The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two‐parameter estimator based on prior information for the vector of parameters is ...
Adewale F. Lukman, Kayode Ayinde, Sek Siok Kun, Emmanuel T. Adewuyi, Zhiping Qiu   +4 more
wiley   +1 more source

Selection of A New Biasing Parameter for the Jackknife Kibria-Lukman Estimator for the Negative Binomial Regression Model

, 2023
The negative binomial regression model (NBRM) is a generalized linear model which relaxes the restrictive assumption by the Poisson regression model when the variance is equal to the mean. The estimation of the parameters of the NBRM is obtained using the maximum likelihood (ML) method. Maximum likelihood estimator becomes unstable when the explanatory
Oranye Henrietta E, Adejuwon Samuel O, Arum Kingsley C, Ugwuowo Fidelis I, Ugah Tobias E, Adegoke Taiwo M, Sule Omeiza B   +6 more
openaire   +1 more source

Performance of some estimators for the multicollinear logistic regression model: theory, simulation, and applications

Research in Statistics
This article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator,
Md Ariful Hoque, B. M. Golam Kibria
doaj   +1 more source

Negative Binomial Regression Model Estimation Using Stein Approach: Methods, Simulation, and Applications

Journal of Mathematics, Volume 2025, Issue 1, 2025.
The negative binomial regression model (NBRM) is popular for modeling count data and addressing overdispersion issues. Generally, the maximum likelihood estimator (MLE) is used to estimate the NBRM coefficients. However, when the explanatory variables in the NBRM are correlated, the MLE yields inaccurate estimates.
Bushra Ashraf, Muhammad Amin, Walid Emam, Yusra Tashkandy, Muhammad Faisal, Qiang Wu   +5 more
wiley   +1 more source

Weighted robust improved KL M-estimators for linear regression model in presence of multicollinearity and outliers: simulation and applications

Research in Statistics
In the presence of multicollinearity and outliers, the ordinary least squares (OLS) estimator becomes unstable. In addition, existing ridge and robust ridge estimators tend to become ineffective when there is significant contamination.
Danish Wasim, Qamruz Zaman, Sidra Nawaz, B. M. Golam Kibria   +3 more
doaj   +1 more source

Improving Confidence Interval Estimation in Logistic Regression with Multicollinear Predictors: A Comparative Study of Shrinkage Estimators and Application to Prostate Cancer Data

Stats
In logistic regression with finite binary samples and multicollinear predictors, the maximum likelihood estimator often results in overfitting and high mean squared error (MSE).
Sultana Mubarika Rahman Chowdhury, Zoran Bursac, B. M. Golam Kibria   +2 more
doaj   +1 more source