Results 51 to 60 of about 173 (111)

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

Mitigating multicollinearity in zero-inflated negative binomial regression using the modified Kibria-Lukman estimator

AIMS Mathematics
Multicollinearity presents a significant challenge in zero-inflated negative binomial (ZINB) regression, leading to unstable maximum likelihood estimates (MLEs) and inflated prediction errors. To address this issue, we investigated the performance of the
Masad A. Alrasheedi, Adewale F. Lukman, Rasha A. Farghali, Asamh Saleh M. Al Luhayb   +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

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   +1 more source

DEVELOPMENT OF SOME RIDGE ESTIMATORS FOR CLASSICAL AND GENERALIZED LINEAR REGRESSION MODELS

, 2022
Regression analysis is a statistical tool usually used to study the relationship between a dependent variable and independent variables. The classical linear regression model (CLRM) and Generalized Linear Regression Model (GLRM) are examples of ...
ALADEITAN, BOLUWAJI BENEDICTA
core   +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

Special ridge-type estimator: Simulation and application to chemical data

AIP Advances
This study delves into regularization techniques, such as ridge regression, Liu estimator, and Kibria–Lukman estimator, as alternatives to the maximum likelihood method for addressing multicollinearity in beta regression models.
Rasha A. Farghali, Adewale F. Lukman, Haadi Abdul-Rahaman, Zakariya Yahya Algamal, Samah M. Abo-El-Hadid   +4 more
doaj   +1 more source

Factors Affecting the Number of Infant Morality Cases in West Java for the 2019-2020 Period using Generalized Poisson Regression (GPR) [PDF]

, 2023
The number of infant mortality cases is data in the form of counts which is modeled by Poisson regression. There is an assumption that needs to be met, namely equidispersion.
Dewi, Kartika, Gusriani, Nurul, Parmikanti, Kankan   +2 more
core   +2 more sources

Mitigating multicollinearity in ridge regression: a comparative study of quantile-based two parameter weighted KL-estimators

Research in Statistics
In linear regression, when predictors exhibit collinearity, the problem of multicollinearity arises, leading to a reduction in the efficiency of the ordinary least squares (OLS) estimator.
Qamruz Zaman, Danish Wasim, B. M. Golam Kibria, Maha Shabbir, Naveed Ullah   +4 more
doaj   +1 more source

Performance Of Some New Quantile-Based Two Parameter Ridge Estimators For Linear Regression Model: Simulation And Application [PDF]

In regression analysis, the efficiency of the ordinary least square (OLS) estimator decreases when the predictors become highly correlated leading to the problem of multicollinearity.
Bushra Haider, Syed Muhammad Asim, Danish Wasim, Naveed Ullah, Qamruz Zaman
core   +2 more sources