Properties of shrinkage estimators in linear regression when disturbances are not normal

DOI10.1016/0304-4076(83)90053-2zbMath0508.62064OpenAlexW2171025170MaRDI QIDQ1838008

Yanyan Li

Publication date: 1983

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1753&context=economicsresrpt

zbMATH Keywords

shrinkage estimatorsbiased estimators of regression coefficientsnon normal case

Mathematics Subject Classification ID

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

Related Items (12)

THE APPROXIMATE MOMENTS OF THE LEAST SQUARES ESTIMATOR FOR THE STATIONARY AUTOREGRESSIVE MODEL UNDER A GENERAL ERROR DISTRIBUTION ⋮ Testing slope homogeneity in panel data models with a multifactor error structure ⋮ Nonlinear unbiased estimation in the linear regression model with nonnormal disturbances ⋮ Small sample properties of ridge estimators with normal and non-normal disturbances ⋮ Large sample asymptotic properties of the double k-class estimators in linear regression models ⋮ The coefficient of determination and its adjusted version in linear regression models ⋮ A modified generalized mixed regression estimator when disturbances are nonnormal ⋮ Linear shrinkage estimation of high-dimensional means ⋮ A synthesis of stein-rule and mixed regression procedures inlinear regression models under non-normality ⋮ The sampling distribution of shrinkage estimators and their F-ratios in the regression model ⋮ Expectation of quadratic forms in normal and nonnormal variables with applications ⋮ The exact density of nonparametric regression estimators: fixed design case

Cites Work

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• Minimax estimation of location parameters for spherically symmetric unimodal distributions under quadratic loss
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• Minimax Adaptive Generalized Ridge Regression Estimators
• Multicollinearity and the Minimax Conditions of the Bock Stein-like Estimator
• Some inequalities on characteristic roots of matrices
• Comparison of k-Class Estimators When the Disturbances Are Small

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