Asymptotic expansion and asymptotic robustness of the normal-theory estimators in the random regression model

DOI10.1080/10629360600679383zbMath1127.62017OpenAlexW2014380952MaRDI QIDQ5438716

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Publication date: 28 January 2008

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10629360600679383

zbMATH Keywords

tablesEdgeworth expansioncovariance structurenon-normalityasymptotic robustnessresidual variancerandom regression model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (1)

The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior

Cites Work

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• Asymptotic robustness of the asymptotic biases in structural equation modeling
• Asymptotic distribution of the reduced rank regression estimator under general conditions
• Asymptotic expansion of the sample correlation coefficient under nonnormality
• Asymptotic expansions for the distributions of the sample roots under nonnormality
• A Tale of Two Regressions
• XX.—On the Theory of Statistical Regression
• Robustness of normal theory methods in the analysis of linear latent variate models
• TWO APPLICATIONS OF BIVARIATE k-STATISTICS
• The bootstrap and Edgeworth expansion

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