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Asymptotic Robustness Study Of The Polychoric Correlation Estimation

机译:多元相关估计的渐近鲁棒性研究

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摘要

Asymptotic robustness against misspecification of the underlying distribution for the polychoric correlation estimation is studied. The asymptotic normality of the pseudo-maximum likelihood estimator is derived using the two-step estimation procedure. The t distribution assumption and the skew-normal distribution assumption are used as alternatives to the normal distribution assumption in a numerical study. The numerical results show that the underlying normal distribution can be substantially biased, even though skewness and kurtosis are not large. The skew-normal assumption generally produces a lower bias than the normal assumption. Thus, it is worth using a non-normal distributional assumption if the normal assumption is dubious.
机译:研究了针对多态相关估计的针对基础分布的错误指定的渐进鲁棒性。伪最大似然估计器的渐近正态性使用两步估计过程得出。在数值研究中,将t分布假设和偏正态分布假设用作正态分布假设的替代方法。数值结果表明,即使偏度和峰度不大,基本正态分布也可能有明显偏差。偏态正态假设通常会产生比正态假设更低的偏差。因此,如果正态假设是可疑的,则值得使用非正态分布假设。

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