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Statistical theories of goodness-of-fit tests in structural equation modeling are based on asymptotic distributions of test statistics. When the model includes a large number of variables or the population is not from a multivariate normal distribution, the asymptotic distributions do not approximate the distribution of the test statistics very well at small sample sizes. A variety of methods have been developed to improve the accuracy of hypothesis testing at small sample sizes. However, all these methods have their limitations, specially for nonnormal distributed data. We propose a Monte Carlo test that is able to control Type I error with more accuracy compared to existing approaches in both normal and nonnormally distributed data at small sample sizes. Extensive simulation studies show that the suggested Monte Carlo test has a more accurate observed significance level as compared to other tests with a reasonable power to reject misspecified models.  相似文献   
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In this study, drained and undrained triaxial tests under isotropic and anisotropic consolidations were conducted on reconstituted samples of Babolsar sand, which underlies a densely populated, seismic region of the southern coast of the Caspian Sea, Mazandaran, Iran. It was demonstrated that the sand experienced all possible states of liquefiable soil: flow failure, limited flow, and dilation. The steady-state and flow liquefaction lines of this sand were presented and compared with previously tested sands. It is shown that the initial stress anisotropy can affect the potential of volume change and pore pressure generation. The steady-state line (SSL), however, remains identical for the isotropically and anisotropically consolidated specimens under drained and undrained conditions. The tests data were then analyzed in order to investigate the liquefaction susceptibility of this sand in terms of parameters such as the state parameter, relative state parameter index, and lateral earth pressure ratio at failure.  相似文献   
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