Compound Regression and Constrained Regression: Nonparametric Regression Frameworks for EIV Models

DOI10.1080/00031305.2018.1556734OpenAlexW2908690624MaRDI QIDQ5869283

Ling Leng, Wei Zhu

Publication date: 28 September 2022

Published in: The American Statistician (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00031305.2018.1556734

zbMATH Keywords

maximum likelihood methodconstrained regressionorthogonal regressionordinary least squares regressiongeometric mean regressioncompound regression

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Constrained optimal designs
• Higher moment estimators for linear regression models with errors in the variables
• The geometric mean procedure for estimating the scale of a judgement matrix
• Properties of the Geometric Mean Functional Relationship
• Likelihood estimation of a simple linear regression model when both variables have error
• The reduced major axis of a bivariate sample
• On the Equivalence of Constrained and Compound Optimal Designs
• Bias in Slope Estimates for the Linear Errors in Variables Model by the Variance Ratio Method
• Measurement Error in Nonlinear Models
• Errors of Prediction in Multiple Regression with Stochastic Regressor Variables
• Measurement Error Models with Auxiliary Data

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