Risk and Pitman closeness properties of feasible generalized double \(k\)-class estimators in linear regression models with non-spherical disturbances under balanced loss function

DOI10.1016/j.jmva.2003.09.011zbMath1051.62058OpenAlexW1984933781MaRDI QIDQ1882938

Yanyan Li

Publication date: 1 October 2004

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2003.09.011

zbMATH Keywords

tablesPitman closenessBalanced loss functionDouble \(k\)-class estimator

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Monte Carlo methods (65C05) Statistical decision theory (62C99)

Related Items (7)

Estimation of a subset of regression coefficients of interest in a model with non-spherical disturbances ⋮ Shrinkage estimation in spatial autoregressive model ⋮ Goodness of fit for generalized shrinkage estimation ⋮ Pitman closest equivariant estimators and predictors under location-scale models ⋮ Performance of double \(k\)-class estimators for coefficients in linear regression models with non-spherical disturbances under asymmetric losses ⋮ Stein-rule estimation under an extended balanced loss function ⋮ A note on Stein-type shrinkage estimator in partial linear models

Cites Work

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• Pitman closeness for Stein-rule estimators of regression coefficients
• Estimation of regression coefficients subject to exact linear restrictions when some observations are missing and quadratic error balanced loss function is used
• Inadmissibility of the Stein-rule estimator under the balanced loss function
• Selecting a double \(k\)-class estimator for regression coefficients
• Improved estimation under Pitman's measure of closeness
• Minimum mean squared error estimation of each individual coefficient in a linear regression model
• The finite sample properties of simultaneous equations' estimates and estimators. Bayesian and non-Bayesian approaches
• Pitman nearness comparisons of Stein-type estimators for regression coefficients in replicated experiments
• On estimation with balanced loss functions
• The exact risks of some pre-test and stein-type regression estimators umder balanced loss
• Comparison of Linear Estimators Using Pitman's Measure of Closeness
• The Minimum Mean Square Error Linear Estimator and Ridge Regression
• Double k-Class Estimators of Coefficients in Linear Regression
• Exact small sample properties of an operational variant of the minimum mean squared error estimator
• The exact risk performance of a pre-test estimator in a heteroskedastic linear regression model under the balanced loss function
• The pitman nearness criterion and its determination
• Risk comparison of the inequality constrained least squares and other related estimators under balanced loss
• Double \(k\)-class estimators in regression models with non-spherical disturbances

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