Asymptotic expansions in mean and covariance structure analysis

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Asymptotic expansions in mean and covariance structure analysis

1. Abstract Asymptotic expansions of the distributions of parameter estimators in mean and covariance structures are derived. The parameters may be common to or specific in means and covariances of observable variables. The means are possibly structured by the common/specific parameters. First, the distributions of the parameter estimators standardized by the population asymptotic standard erro...

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The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed, and hence classical multivariate analysis, based on multinormal error-free variables having no simultaneous interrelations, is not adequate to deal with such data. S...

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Robust Methods for Mean and Covariance Structure Analysis

Covariance structure analysis plays an important role in social and behavioral sciences to evaluate hypothesized in uences among unmeasured latent and observed variables. Existing methods for analyzing these data rely on unstructured sample means and covariances estimated under normality, and evaluate a proposed structural model using statistical theory based on normal theory MLE and generalize...

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Mean and Covariance Structure Analysis with Missing Data

Most of the existing methods for missing data analysis are density based imputations. A serious drawback of these methods is that, when the observed data do not obey the assumed density, consequent inferences may not be reliable. In the context of mean and covariance structure analysis with missing data, use of a pseudo-maximum likelihood method has been proposed, but its properties have not be...

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Asymptotic expansions for a class of tests for a general covariance structure under a local alternative

Let S be a p × p random matrix having a Wishart distribution Wp(n, n−1Σ). For testing a general covariance structureΣ = Σ(ξ), we consider a class of test statistics Th = nρh(S,Σ(ξ̂)), where ρh(Σ1,Σ2) = ∑p i=1 h(λi) is a distance measure from Σ1 to Σ2, λi’s are the eigenvalues of Σ1Σ 2 , and h is a given function with certain properties. Wakaki, Eguchi, Fujikoshi (1990) suggested this class and g...

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ژورنال

عنوان ژورنال: Journal of Multivariate Analysis

سال: 2009

ISSN: 0047-259X

DOI: 10.1016/j.jmva.2008.09.001