Asymptotic robustness of the asymptotic biases in structural equation modeling

DOI10.1016/j.csda.2004.06.002zbMath1429.62222OpenAlexW2020111686MaRDI QIDQ957243

Haruhiko Ogasawara

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.06.002

zbMATH Keywords

principal component analysisfactor analysisasymptotic robustnessasymptotic biasesLISREL model

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (5)

Asymptotic Expansion in Reduced Rank Regression Under Normality and Nonnormality ⋮ Asymptotic expansions in the singular value decomposition for cross covariance and correlation under nonnormality ⋮ Maximum likelihood methods in treating outliers and symmetrically heavy-tailed distributions for nonlinear structural equation models with missing data ⋮ Asymptotic expansion and asymptotic robustness of the normal-theory estimators in the random regression model ⋮ Asymptotic Expansions of the Null Distributions of Discrepancy Functions for General Covariance Structures Under Nonnormality

Uses Software

• Amos

Cites Work

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• Linear latent variable models and covariance structures
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• A study of algorithms for covariance structure analysis with specific comparisons using factor analysis
• Model conditions for asymptotic robustness in the analysis of linear relations
• Asymptotic biases in exploratory factor analysis and structural equation modeling
• Factor analysis by generalized least squares
• The weight matrix in asymptotic distribution-free methods
• Robustness of statistical inference in factor analysis and related models
• Robustness of normal theory statistics in structural equation models*
• ASYMPTOTIC ROBUSTNESS IN MULTIPLE GROUP LINEAR-LATENT VARIABLE MODELS
• Asymptotically distribution‐free methods for the analysis of covariance structures
• Robustness of normal theory methods in the analysis of linear latent variate models
• Topics in Applied Multivariate Analysis

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