Results 31 to 40 of about 218 (135)
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
Robust-M new two-parameter estimator for linear regression models: Simulations and applications [PDF]
, 2023 In the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously.
A. A. Akomolafe +4 more
core +2 more sources
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
Efficient estimation and validation of shrinkage estimators in big data analytics [PDF]
, 2023 DATA AVAILABILITY STATEMENT: Data is available from the authors on request.Shrinkage estimators are often used to mitigate the consequences of multicollinearity in linear regression models.
Arashi, Mohammad +3 more
core +1 more source
On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model [PDF]
, 2022 The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity problem,
Abiola Timothy Owolabi +3 more
core +2 more sources
Restricted ride estimator in the Inverse Gaussian regression model [PDF]
, 2022 The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. Its parameters are usually estimated using maximum likelihood (ML) method.
Algamal, Zakariya Yahya, Alsarraf, Israa
core +5 more sources
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
A Comparative Study of Ridge, LASSO and Elastic net Estimators [PDF]
, 2021 The focus of this thesis is to review the three basic penalty estimators, namely, ridge regression estimator, LASSO, and elastic net estimator in the light of the deficiencies of least-squares estimator.
Al Dabal, Meaad Abdullah A.
core +2 more sources