期刊
JOURNAL OF MULTIVARIATE ANALYSIS
卷 98, 期 6, 页码 1262-1282出版社
ELSEVIER INC
DOI: 10.1016/j.jmva.2006.08.005
关键词
Kolmogorov-Smirnov distance; noncentral chi-square distribution; normal distribution; quantile-quantile plot; structural model
The normal distribution based likelihood ratio (LR) statistic is widely used in structural equation modeling. Under a sequence of local alternative hypotheses, this statistic has been shown to asymptotically follow a noncentral chi-square distribution. In practice, the population mean vector and covariance matrix as well as the model and sample size are always fixed. It is hard to justify the validity of the noncentral chi-square distribution for the resulting LR statistic even when data are normally distributed and sample size is large. By extending results in the literature, this paper develops normal distributions to describe the behavior of the LR statistic for mean and covariance structure analysis. A sequence of local alternative hypotheses is not necessary for the proposed distributions to be asymptotically valid. When the effect size is medium and above or when the model is not trivially misspecified, empirical results indicate that a refined normal distribution describes the behavior of the LR statistic better than the commonly used noncentral chi-square distribution, as measured by the Kolmogorov-Smirnov distance. Quantile-quantile plots are also provided to better understand the different distributions. (c) 2006 Elsevier Inc. All rights reserved.
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