On predicting the population total under regression models with measurement errors

DOI10.1016/0378-3758(95)00181-6zbMath0860.62009OpenAlexW2057960868MaRDI QIDQ1817382

Heleno Bolfarine, Mônica C. Sandoval, Shelemyahu Zacks

Publication date: 23 April 1997

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0378-3758(95)00181-6

zbMATH Keywords

asymptotic normalitybest unbiased predictorfinite populationsexplanatory variablesordinary least-squares estimatorprediction of population totalpredictive variancesregression superpopulation models

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Sampling theory, sample surveys (62D05)

Related Items (5)

Admissible prediction in superpopulation models with random regression coefficients under matrix loss function ⋮ Empirical Bayes Estimation of Small Area Means under a Nested Error Linear Regression Model with Measurement Errors in the Covariates ⋮ Optimal prediction in finite populations under matrix loss ⋮ Estimating realized random effects ⋮ Admissible Linear Predictors in the Superpopulation Model with Respect to Inequality Constraints

Cites Work

• The limiting distribution of least squares in an errors-in-variables regression model
• Prediction theory for finite populations
• Estimation of a structural linear regression model with a known reliability ratio
• Approximation Theorems of Mathematical Statistics
• Comparison of Least Squares and Errors-in-Variables Regression, With Special Reference to Randomized Analysis of Covariance
• Errors of Measurement in Statistics
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