Results 31 to 40 of about 34,185 (288)

Kibria-Lukman Estimator for General Linear Regression Model with AR(2) Errors: A Comparative Study with Monte Carlo Simulation

Journal of New Theory, 2022
The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with the multicollinearity, Ridge, Liu, and Ridge-type biased estimators have been presented in the statistical ...
Tuğba Söküt Açar
doaj   +1 more source

New estimators in a partial linear model depending on an unbiased ridge regression estimator [PDF]

EPJ Web of Conferences
This paper introduces two new estimators based on the philosophy of unbiased ridge regression estimation, where the parameters are part of a partial linear model suffering from multicollinearity.
Al-Khazraji Yousif A., AIheety Mustafa I.   +1 more
doaj   +1 more source

Modified Kibria-Lukman (MKL) estimator for the Poisson Regression Model: application and simulation [version 2; peer review: 2 approved, 1 approved with reservations]

F1000Research, 2021
Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe, Benedicta B. Aladeitan, Adewale F. Lukman, Oluwakemi E. Abiodun, Olajumoke Oludoun   +4 more
doaj   +1 more source

Improving generalized ridge estimator for the gamma regression model. [PDF]

المجلة العراقية للعلوم الاحصائية
It has been consistently proven that the ridge estimator is an effective shrinking strategy for reducing the effects of multicollinearity. An effective model to use when the response variable is positively skewed is the Gamma Regression Model (GRM ...
AVAN Al-Saffar, Zakaria Y. Algamal
doaj   +1 more source

A lava attack on the recovery of sums of dense and sparse signals [PDF]

, 2015
Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with ...
Chernozhukov, Victor, Hansen, Christian, Liao, Yuan   +2 more
core   +3 more sources

Ridge Regression and Ill-Conditioning [PDF]

, 2014
Hoerl and Kennard (1970) suggested the ridge regression estimator as an alternative to the Ordinary Least Squares (OLS) estimator in the presence of multicollinearity.
Iguernane, Mohamed, Khalaf, Ghadban
core   +2 more sources

Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model

The Scientific World Journal, 2020
The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter.
Adewale F. Lukman, Kayode Ayinde, B. M. Golam Kibria, Segun L. Jegede   +3 more
doaj   +1 more source

M Robust Weighted Ridge Estimator in Linear Regression Model

African Scientific Reports, 2023
Correlated regressors are a major threat to the performance of the conventional ordinary least squares (OLS) estimator. The ridge estimator provides more stable estimates in this circumstance.
Taiwo Stephen Fayose, Kayode Ayinde, Olatayo Olusegun Alabi   +2 more
doaj   +1 more source

The Comparison Between Different Approaches to Overcome the Multicollinearity Problem in Linear Regression Models

Ibn Al-Haitham Journal for Pure and Applied Sciences, 2018
    In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.
Hazim Mansoor Gorgees, Fatimah Assim Mahdi   +1 more
doaj   +1 more source

A New Type Iterative Ridge Estimator: Applications and Performance Evaluations

Journal of Mathematics, 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 ...
Aydın Karakoca
doaj   +1 more source