A note on almost unbiased generalized ridge regression estimator under asymmetric loss

DOI10.1080/00949659908811943zbMath0936.62078OpenAlexW2030876816MaRDI QIDQ4942509

Alan T. K. Wan

Publication date: 18 May 2000

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659908811943

zbMATH Keywords

tablesridge regressionriskLINEX lossrelative efficiency

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (11)

New biased estimators under the LINEX loss function ⋮ Regression models using the LINEX loss to predict lower bounds for the number of points for approximating planar contour shapes ⋮ An iterative feasible minimum mean squared error estimator of the disturbance variance in linear regression under asymmetric loss ⋮ Multicollinearity and financial constraint in investment decisions: a Bayesian generalized ridge regression ⋮ Generalized Liu Type Estimators Under Zellner's Balanced Loss Function ⋮ Minimax invariant estimator of continuous distribution function under LINEX loss ⋮ Stein-type improved estimation of standard error under asymmetric LINEX loss function ⋮ Risk performance of a pre-test ridge regression estimator under the LINEX loss function when each individual regression coefficient is estimated ⋮ Minimax and \(\Gamma\)-minimax estimation for the Poisson distribution under LINEX loss when the parameter space is restricted ⋮ On the sampling performance of an inequality pre-test estimator of the regression error variance under LINEX loss ⋮ On generalized ridge regression estimators under collinearity and balanced loss

Uses Software

• NAG
• nag

Cites Work

• A class of almost unbiased and efficient estimators of regression coefficients
• Local asymptotic minimax risk bounds for asymmetric loss functions
• Admissible estimation for finite population under the Linex loss function
• Generalized ridge regression estimators under the LINEX loss function
• Risk of a homoscedasticity pre-test estimator of the regression scale under LINEX loss
• On small sample properties of the almost unbiased generalized ridge estimator
• A simulation study of ridge and other regression estimators
• Distribution and density functions of the feasible generalized ridge regression estimator

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