Least squares estimators in measurement error models under the balanced loss function.

DOI10.1007/BF02595699zbMath1109.62326OpenAlexW2017860359MaRDI QIDQ1872839

Yanyan Li

Publication date: 18 May 2003

Published in: Test (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02595699

zbMATH Keywords

measurement errorsBalanced loss functiondirect and reverse regressionultra structural model

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Linear inference, regression (62J99)

Related Items (6)

Efficiency of the restricted \(r\)-\(d\) class estimator in linear regression ⋮ Admissibility in general Gauss–Markov model with respect to an ellipsoidal constraint under weighted balanced loss ⋮ Ridge regression methodology in partial linear models with correlated errors ⋮ Admissibility in general linear model with respect to an inequality constraint under balanced loss ⋮ 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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• Asymptotic efficiency properties of least squares in an ultrastructural model
• The exact risks of some pre-test and stein-type regression estimators umder balanced loss
• The Importance of Assessing Measurement Reliability in Multivariate Regression
• The ultrastructural relation: A synthesis of the functional and structural relations
• Risk comparison of the inequality constrained least squares and other related estimators under balanced loss

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