Monte Carlo Simulation Study of Biased Estimators in the Linear Regression Models with Correlated or Heteroscedastic Errors

DOI10.1080/03610918.2012.728273zbMath1291.62133OpenAlexW2056557004MaRDI QIDQ5415910

M. Revan Özkale

Publication date: 19 May 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.728273

zbMATH Keywords

ridge estimatorprincipal components regression estimatorautocorrelated errorshrunken estimatoriteration estimatorjackknifed ridge estimator

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (1)

The feasible generalized restricted ridge regression estimator

Cites Work

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• Nonnegative and positive definiteness of matrices modified by two matrices of rank one
• A jackknifed ridge estimator in the linear regression model with heteroscedastic or correlated errors
• On the performance of biased estimators in the linear regression model with correlated or heteroscedastic errors
• A Simulation Study of Ridge Regression Estimators with Autocorrelated Errors
• Mean squared error matrix comparisons between biased estimators — An overview of recent results
• Ridge estimation in regression problems with autocorrelated errors: A monte carlo study
• Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation

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