Ridge regression estimator: combining unbiased and ordinary ridge regression methods of estimation [PDF]
Surveys in Mathematics and its Applications, 2009 Statistical literature has several methods for coping with multicollinearity. This paper introduces a new shrinkage estimator, called modified unbiased ridge (MUR).
Sharad Damodar Gore, Feras Sh. M. Batah
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A New Mixed Biased Estimator for Ill‐Conditioning Challenges in Linear Regression Model With Chemometrics Applications [PDF]
Analytical Science AdvancesIn linear regression models, the ordinary least squares (OLS) method is used to estimate the unknown regression coefficients. However, the OLS estimator may provide unreliable estimates in non‐orthogonal models.
Muhammad Amin +3 more
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Transfer Learning for Moderate–Dimensional Ridge-Regularized Robust Linear Regression [PDF]
EntropyThis paper studies transfer learning for ridge-regularized robust linear regression in the moderate–dimensional regime, where the number of predictors is of the same order as the sample size and the regression coefficients are not assumed to be sparse ...
Lingfeng Lyu, Xiao Guo, Zongqi Liu
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A stochastic restricted ridge regression estimator [PDF]
Journal of Multivariate Analysis, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Özkale M.R.
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New robust estimator for handling outliers and multicollinearity in gamma regression model with application to breast cancer data [PDF]
Scientific ReportsThe gamma regression model (GRM) is commonly used to analyze continuous data that are positively skewed. However, the GRM is sensitive to multicollinearity and outliers. These two problems often occur in regression analysis.
Arwa M. Alshangiti +7 more
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Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications [PDF]
Iraqi Journal for Computer Science and Mathematics, 2023 Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent.
Zakariya Algamal +3 more
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Development of the generalized ridge estimator for the Poisson-Inverse Gaussian regression model with multicollinearity [PDF]
Scientific ReportsThe Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be ...
Fatimah A. Almulhim +5 more
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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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Robust weighted ridge regression based on S – estimator [PDF]
African Scientific Reports, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Taiwo Stephen Fayose +3 more
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A bias-reduced estimator for generalized Poisson regression with application to carbon dioxide emission in Canada [PDF]
Scientific ReportsThe generalized Poisson regression model (GPRM) provides a flexible framework for modeling count data, especially those exhibiting over- or underdispersion.
Fatimah M. Alghamdi +6 more
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