Results 41 to 50 of about 551 (134)
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
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
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
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
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
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
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
Comparison of estimators efficiency for linear regressions with joint presence of autocorrelation and multicollinearity [PDF]
, 2021 This paper proposes a new estimator called Two stage K-L estimator by combining these two estimators previously proposed by Prais Winsten (1958) and Kibra with Lukman (2020) for autocorrelation and multicollinearity respectively and to derived the ...
Adenomon, Monday Osagie +1 more
core +1 more source