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Multivariate Regression with Errors in Variables: Issues on Asymptotic Robustness

  • Albert Satorra
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 120)

Abstract

Estimation and testing of functional or structural multivariate regression with errors in variables, with possibly unbalanced design for replicates, and not necessarily normal data, is developed using only the sample cross-product moments of the data. We give conditions under which normal theory standard errors and an asymptotic chi-square goodness-of-fit test statistic retain their validity despite non-normality of constituents of the model. Assymptotic optimality for a subvector of parameter estimates is also investigated. The results developed apply to methods that are widely available in standard software for structural equation models, such as LISREL or EQS.

Keywords

Functional Model Normality Assumption Covariance Structure Analysis Moment Structure Linear Latent Variable Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1997

Authors and Affiliations

  • Albert Satorra
    • 1
  1. 1.Departament d’EconomiaUniversitat Pompeu FabraBarcelonaSpain

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