Results 31 to 40 of about 133 (94)
Axioms, 2022 The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud +3 more
doaj +1 more source
A new hybrid estimator for linear regression model analysis: Computations and simulations
Scientific African, 2023 The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj +1 more source
Predictive Performance Evaluation of the Kibria-Lukman Estimator
WSEAS TRANSACTIONS ON MATHEMATICS, 2022 Regression models are commonly used in prediction, but their predictive performances may be affected by the problem called the multicollinearity. To reduce the effect of the multicollinearity, different biased estimators have been proposed as alternatives to the ordinary least squares estimator.
Issam Dawoud +2 more
openaire +1 more source
Robust biased estimators for Poisson regression model: Simulation and applications
Concurrency and Computation: Practice and Experience, Volume 35, Issue 7, 25 March 2023., 2023 Summary The method of maximum likelihood flops when there is linear dependency (multicollinearity) and outlier in the generalized linear models. In this study, we combined the ridge estimator with the transformed M‐estimator (MT) and the conditionally unbiased bounded influence estimator (CE).
Adewale F. Lukman +2 more
wiley +1 more source
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022 The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error.
Aydın Karakoca, Niansheng Tang
wiley +1 more source
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 +4 more
wiley +1 more source
Mitigating Multicollinearity in Linear Regression Model with Two Parameter Kibria-Lukman Estimators
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL, 2023 This study delves into the challenges faced by the ordinary least square (OLS) estimator, traditionally regarded as the Best Linear Unbiased Estimator in classical linear regression models. Despite its reliability under specific conditions, OLS falters in the face of multicollinearity, a problem frequently encountered in regression analyses.
Idowu J. I. +5 more
openaire +1 more source
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 +1 more
doaj +1 more source
A Liu estimator for the beta regression model and its application to chemical data
Journal of Chemometrics, Volume 34, Issue 10, October 2020., 2020 Abstract Beta regression has become a popular tool for performing regression analysis on chemical, environmental, or biological data in which the dependent variable is restricted to the interval [0, 1]. For the first time, in this paper, we propose a Liu estimator for the beta regression model with fixed dispersion parameter that may be used in several
Peter Karlsson +2 more
wiley +1 more source
Scientific African, 2022 The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by lots of authors.
Issam Dawoud +2 more
doaj +1 more source