Abstract

This article focuses on the idea of recognizing distributions rather than performing classic goodness-of-fit tests (GoFTs). In order to recognize distributions, the method of potential functions (MoPF) is used, focusing the reader’s attention on recognizing the normal distribution. The prevailing part of the article concentrates on the implementation of a classifier of distributions that involves MoPF. Recognizing distributions is supported by numerous examples of simulation and real data examples. GoFTs are conservative. When the test statistics exceeds relevant critical value, there are reasons to reject H0.$H_0.$$H_0.$ What next? The answer is: Recognizing distributions by means of the MoPF.

摘要

本文重点讨论识别分布的想法，而不是执行经典的拟合优度检验 (GoFT)。为了识别分布，使用了势函数方法（MoPF），将读者的注意力集中在识别正态分布上。本文的主要部分集中于涉及 MoPF 的分布分类器的实现。大量模拟示例和真实数据示例支持识别分布。GoFT 是保守的。当检验统计量超过相关临界值时，就有理由拒绝H0。$H_0。$$H_0.$接下来是什么？答案是：通过 MoPF 来识别分布。