Results 61 to 70 of about 551 (134)

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

Potential RNA-dependent RNA polymerase inhibitors as prospective therapeutics against SARS-CoV-2 [PDF]

, 2020
Introduction. The emergence of SARS-CoV-2 has taken humanity off guard. Following an outbreak of SARS-CoV in 2002, and MERS-CoV about 10 years later, SARS-CoV-2 is the third coronavirus in less than 20 years to cross the species barrier and start ...
Chapagain, Prem, Pokhrel, Rudramani, Siltberg-Liberles, Jessica   +2 more
core   +1 more source

Modeling of COVID-19 Cases in Indonesia with the Method of Geographically Weighted Regression [PDF]

, 2023
The COVID-19 pandemic has spread to all corners of the world, including Indonesia. Various factors affect the spread of COVID-19 cases in an area so that the government and the community can make prevention and control efforts so that this pandemic does ...
Arifin, Samsul, Herdiani, Erna Tri
core   +2 more sources

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

INVERSE GAUSSIAN REGRESSION MODELING AND ITS APPLICATION IN NEONATAL MORTALITY CASES IN INDONESIA [PDF]

, 2022
Inverse Gaussian Regression (IGR) is a suitable model for modeling positively skewed response data, which follows the inverse Gaussian distribution. The IGR model was formed from the Generalized Linear Models (GLM).
Fathurahman, M.
core   +2 more sources

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

Two Stage Robust Ridge Method in a Linear Regression Model [PDF]

, 2015
Two Stage Robust Ridge Estimators based on robust estimators M, MM, S, LTS are examined in the presence of autocorrelation, multicollinearity and outliers as alternative to Ordinary Least Square Estimator (OLS).
Ayinde, Kayode, Lukman, Adewale Folaranmi, Osowole, Oyedeji Isola   +2 more
core   +3 more sources

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

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

Detection of Near-Nulticollinearity through Centered and Noncentered Regression [PDF]

, 2020
This paper analyzes the diagnostic of near-multicollinearity in a multiple linear regression from auxiliary centered (with intercept) and noncentered (without intercept) regressions.
García García, Catalina, García Pérez, José, Salmerón Gómez, Roman   +2 more
core   +3 more sources