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首页> 外文期刊>Journal of statistical computation and simulation >Goodness-of-fit Testing By Transforming To Normality:comparison Between Classical And Characteristic function-based Methods
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Goodness-of-fit Testing By Transforming To Normality:comparison Between Classical And Characteristic function-based Methods

机译：通过转换为正态性进行拟合优度测试：基于经典和特征函数的方法之间的比较

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摘要

Chen and Balakrishnan [Chen, G. and Balakrishnan, N., 1995, A general purpose approximate goodness-of-fit test. Journal of Quality Technology, 27, 154-161] proposed an approximate method of goodness-of-fit testing that avoids the use of extensive tables. This procedure first transforms the data to normality, and subsequently applies the classical tests for normality based on the empirical distribution function, and critical points thereof. In this paper, we investigate the potential of this method in comparison to a corresponding goodness-of-fit test which instead of the empirical distribution function, utilizes the empirical characteristic function. Both methods are in full generality as they may be applied to arbitrary laws with continuous distribution function, provided that an efficient method of estimation exists for the parameters of the hypothesized distribution.
机译：Chen and Balakrishnan [Chen，G. and Balakrishnan，N.，1995，通用近似拟合优度检验。 《质量技术杂志》，第27卷，第154-161页]提出了一种拟合优度测试的近似方法，该方法避免使用大量表格。该过程首先将数据转换为正态性，然后基于经验分布函数及其临界点对正态性进行经典检验。与相应的拟合优度检验相比，本文研究了该方法的潜力，该检验代替了经验分布函数，而是利用经验特征函数。两种方法都是完全通用的，因为它们可以应用于具有连续分布函数的任意定律，只要对假设分布的参数存在一种有效的估计方法即可。

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