K
Ke-Hai Yuan
Researcher at University of Notre Dame
Publications - 158
Citations - 8211
Ke-Hai Yuan is an academic researcher from University of Notre Dame. The author has contributed to research in topics: Covariance & Population. The author has an hindex of 41, co-authored 150 publications receiving 6977 citations. Previous affiliations of Ke-Hai Yuan include University of California, Berkeley & University of North Texas.
Papers
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Journal ArticleDOI
Three Likelihood-Based Methods For Mean and Covariance Structure Analysis With Nonnormal Missing Data
Ke-Hai Yuan,Peter M. Bentler +1 more
TL;DR: In this article, a two-stage approach based on the unstructured mean and covariance estimates obtained by the EM-algorithm is proposed to deal with missing data in social and behavioral sciences, and the asymptotic efficiencies of different estimators are compared under various assump...
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Structural Equation Modeling with Small Samples: Test Statistics.
Peter M. Bentler,Ke-Hai Yuan +1 more
TL;DR: This article studies the small sample behavior of several test statistics that are based on maximum likelihood estimator, but are designed to perform better with nonnormal data.
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Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation
TL;DR: This study examined univariate and multivariate skewness and kurtosis collected from authors of articles published in Psychological Science and the American Education Research Journal and found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions.
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Fit Indices Versus Test Statistics
TL;DR: Property of the commonly used model fit indices when dropping the chi-square distribution assumptions are studied and linearly approximating the distribution of a fit index/statistic by a known distribution or the distribution under a set of different conditions is proposed.
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Normal theory based test statistics in structural equation modelling
Ke-Hai Yuan,Peter M. Bentler +1 more
TL;DR: This paper proposes three new asymptotically distribution-free (ADF) test statistics that technically must yield improved behaviour in samples of realistic size, and uses Monte Carlo methods to study their actual finite sample behaviour.