Consistent estimation of coefficients in measurement error models with replicated observations

DOI10.1016/S0047-259X(03)00021-6zbMath1026.62052MaRDI QIDQ1403414

D. Massart

Publication date: 1 September 2003

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

zbMATH Keywords

replicated observationsmeasurement errorsultrastructural model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (9)

Estimation of Regression Coefficients in a Restricted Measurement Error Model Using Instrumental Variables ⋮ Restricted estimation in multivariate measurement error regression model ⋮ Coefficient of determination for multiple measurement error models ⋮ Using stochastic prior information in consistent estimation of regression coefficients in replicated measurement error model ⋮ Consistent estimation of regression coefficients in ultrastructural measurement error model using stochastic prior information ⋮ Goodness of fit in restricted measurement error models ⋮ Replicated measurement error model under exact linear restrictions ⋮ Restricted regression estimation in measurement error models ⋮ Use of prior information in the consistent estimation of regression coefficients in measurement error models

Cites Work

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• Improved estimation in measurement error models through Stein rule procedure
• Improved estimation of the slope parameter in a linear ultrastructural model when measurement errors are not necessarily normal
• Consistent estimation for the non-normal ultrastructural model
• Multivariate linear ultrastructural relationships model
• The Importance of Assessing Measurement Reliability in Multivariate Regression
• The linear structural relation for several groups of data
• The ultrastructural relation: A synthesis of the functional and structural relations
• Alternative covariance estimates in a replicated measurement error model with correlated, heteroscedastic errors
• Least Squares and Grouping Method Estimators in the Errors in Variables Model

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