Performance of double \(k\)-class estimators for coefficients in linear regression models with non-spherical disturbances under asymmetric losses

DOI10.1016/j.jmva.2012.05.014zbMath1274.62456OpenAlexW2087165241MaRDI QIDQ450850

J. Herrera, D. Rodríguez-Gómez

Publication date: 26 September 2012

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2012.05.014

zbMATH Keywords

LINEX loss functionmultiple linear regressiondouble \(k\)-class estimator

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (1)

Goodness of fit for generalized shrinkage estimation

Cites Work

• Selecting a double \(k\)-class estimator for regression coefficients
• Risk and Pitman closeness properties of feasible generalized double \(k\)-class estimators in linear regression models with non-spherical disturbances under balanced 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 an adjustment of degrees of freedom in the minimim mean squared error ertimator
• A note on finite population prediction under asymetric loss functions
• Shrinkage Estimation with General Loss Functions: An Application of Stochastic Dominace Theory
• Bayesian Estimation and Prediction Using Asymmetric Loss Functions
• The Minimum Mean Square Error Linear Estimator and Ridge Regression
• Double k-Class Estimators of Coefficients in Linear Regression
• Preliminary-test estimation of the regression scale parameter when the loss function is asymmetric
• Large sample asymptotic properties of the double k-class estimators in linear regression models
• Theory of Preliminary Test and Stein‐Type Estimation With Applications
• Prediction with a Generalized Cost of Error Function
• Double \(k\)-class estimators in regression models with non-spherical disturbances
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