Combining Unbiased Ridge and Principal Component Regression Estimators

DOI10.1080/03610920802503396zbMath1170.62047OpenAlexW2008412239MaRDI QIDQ3396330

M. Revan Özkale, Feras Sh. Mahmood Batah, Sharad D. Gore

Publication date: 18 September 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920802503396

zbMATH Keywords

multicollinearityordinary least squares estimatorprincipal components regression estimatorordinary ridge regression estimator\((r, k)\) class estimatorunbiased ridge regression estimator

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items

Principal component ridge type estimator for the inverse Gaussian regression model ⋮ Shrinkage parameter selection via modified cross-validation approach for ridge regression model ⋮ Combining two-parameter and principal component regression estimators ⋮ A new multicollinearity diagnostic for generalized linear models ⋮ The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses ⋮ A VIF-based optimization model to alleviate collinearity problems in multiple linear regression ⋮ Comparison of some estimators under the Pitman's closeness criterion in linear regression model

Cites Work

• Linear models. Least squares and alternatives
• Comparisons of the Unbiased Ridge Estimation to the Other Estimations
• A ridge-type estimator and good prior means
• Ridge regression:some simulations
• Good ridge estimators based on prior information
• Biased Estimation in Regression: An Evaluation Using Mean Squared Error
• Unbiased ridge estimation with prior information and ridge trace
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
• On Biased Estimation in Linear Models

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