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Boosting Ridge Regression [PDF]
, 2005 Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
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Improving generalized ridge estimator for the gamma regression model. [PDF]
المجلة العراقية للعلوم الاحصائيةIt 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
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A New Two-Parameter Estimator for Beta Regression Model: Method, Simulation, and Application
Frontiers in Applied Mathematics and Statistics, 2022 The beta regression is a widely known statistical model when the response (or the dependent) variable has the form of fractions or percentages. In most of the situations in beta regression, the explanatory variables are related to each other which is ...
Mohamed R. Abonazel +3 more
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A new modified ridge-type estimator for the beta regression model: simulation and application
AIMS Mathematics, 2022 The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable ...
Muhammad Nauman Akram +3 more
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A New Convex Estimator Combining Ridge and Ordinary Least Squares Estimators [PDF]
مجلة جامعة الانبار للعلوم الصرفةIn the presence of high correlation between the independent variables in the linear regression model, which is known as the multicollinearity problem, the ordinary least squares estimator produce large variations in the sample.
Karam Al-janabi, Mustafa Alheety
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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
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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 +1 more
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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 +2 more
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Modified One-Parameter Liu Estimator for the Linear Regression Model
Modelling and Simulation in Engineering, 2020 Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model.
Adewale F. Lukman +3 more
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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 +2 more
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