论文标题
针对已知替代方案的连续分布的卡方拟合测试
A Chi-square Goodness-of-Fit Test for Continuous Distributions against a known Alternative
论文作者
论文摘要
Chi Square拟合测试是最古老的已知统计测试之一，Pearson于1900年首次提出了多项式分布。从那以后，它一直在许多领域使用。但是，各种研究表明，当应用于连续分布的数据时，它通常不如其他方法，例如Kolmogorov-Smirnov或Anderson-Darling测试。但是，Chi Square测试的性能（即功能）至关重要取决于数据的归纳方式。在本文中，我们描述了一种自动发现与特定替代方案非常好的套在一起的方法。我们表明，Chi Square测试通常具有竞争力，有时甚至优于其他标准测试。
The chi square goodness-of-fit test is among the oldest known statistical tests, first proposed by Pearson in 1900 for the multinomial distribution. It has been in use in many fields ever since. However, various studies have shown that when applied to data from a continuous distribution it is generally inferior to other methods such as the Kolmogorov-Smirnov or Anderson-Darling tests. However, the performance, that is the power, of the chi square test depends crucially on the way the data is binned. In this paper we describe a method that automatically finds a binning that is very good against a specific alternative. We show that then the chi square test is generally competitive and sometimes even superior to other standard tests.
