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Notes on Power of Normality Tests of Error Terms in Regression Models

机译：回归模型中误差术语的正常性测试权的注意事项

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摘要

Normality is one of the basic assumptions in applying statistical procedures. For example in linear regression most of the inferential procedures are based on the assumption of normality, i.e. the disturbance vector is assumed to be normally distributed. Failure to assess non-normality of the error terms may lead to incorrect results of usual statistical inference techniques such as t-test or F-test. Thus, error terms should be normally distributed in order to allow us to make exact inferences. As a consequence, normally distributed stochastic errors are necessary in order to make a not misleading inferences which explains a necessity and importance of robust tests of normality. Therefore, the aim of this contribution is to discuss normality testing of error terms in regression models. In this contribution, we introduce the general RT class of robust tests for normality, and present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models.
机译：常态是应用统计程序的基本假设之一。例如，在线性回归大部分的推理过程基于正态假设，即假设干扰矢量为正态分布。故障评估误差项的非正态可能导致通常统计推断方法，例如t检验或F-检验不正确的结果。因此，误差项应该是正态分布的，以使我们能够做出准确的推论。因此，正态分布随机误差，以使不误导推论这解释了正常的健壮测试中的必要性和重要性是必要的。因此，这种贡献的目的是讨论在回归模型误差项的正态测试。在这方面的贡献，我们引入一般RT类的常态稳健的测试，并提出和讨论的功率和在回归模型误差项的选择经典和强大的正态性检验稳健性之间的权衡。

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