Results 41 to 50 of about 173 (111)
Almost Unbiased Ridge Estimator in the Inverse Gaussian Regression Model [PDF]
, 2022 The inverse Gaussian regression (IGR) model is a very common model when the shape of the response variable is positively skewed. The traditional maximum likelihood estimator (MLE) is used to estimate the IGR model parameters.
Al-Taweel, Younus Hazim +1 more
core +5 more sources
Robust weighted ridge regression based on S – estimator [PDF]
, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Abimbola Hamidu Bello +3 more
core +2 more sources
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 +4 more
wiley +1 more source
Kibria-Lukman Hybrid Estimator for the Conway–Maxwell–Poisson Regression Model [PDF]
The Conway-Maxwell-Poisson regression (CMPR) model provides a flexi- ble framework for analyzing count data in cases of over- and under-dispersion. Estimating the parameter in CMPR typically relies on the maximum likeli- hood estimator (MLE), which can ...
Alrweili, Hleil
core +3 more sources
A comparative study between shrinkage methods (ridge-lasso) using simulation [PDF]
, 2023 The general linear model is widely used in many scientific fields, especially biological ones. The Ordinary Least Squares (OLS) estimators for the coefficients of the general linear model are characterized by good specifications symbolized by the acronym
Ghareeb, et. al., Zainab Fadhil
core +5 more sources
Research in StatisticsThis 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 +2 more
core +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
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 +5 more
wiley +1 more source
MathematicsThis study presents a novel estimator that combines the Kibria–Lukman and ridge estimators to address the challenges of multicollinearity in Conway–Maxwell–Poisson (COMP) regression models.
Nasser A. Alreshidi +4 more
doaj +1 more source