重加权和循环化的Anderson-Darling拟合优度检验
Reweighted and Circularized Anderson-Darling Tests of Goodness-of-Fit
AI总结：
本文在重加权Anderson-Darling检验的背景下，通过几何理解、提出循环对称检验（循环化检验）以及建立大样本结果，改进了拟合优度检验的性能。
摘要：
本文研究了重加权Anderson-Darling检验背景下的拟合优度综合检验，并做出了三方面贡献。第一个贡献是提供几何理解。论证了对于可交换分布偏差具有最小方差的检验统计量可以作为良好的通用检验。第二个贡献是提出更好的综合检验，称为循环对称检验，通过循环化重加权Anderson-Darling检验统计量或更一般地基于观测顺序统计量的检验统计量得到。由此产生的检验称为循环化检验。一项有限但具有说服力的有限样本性能模拟研究表明，循环化检验具有良好的性能，因为在模拟研究中它们通常优于其父方法。第三个贡献是建立了新的渐近结果。
英文摘要：
This paper takes a look at omnibus tests of goodness of fit in the context of reweighted Anderson-Darling tests and makes threefold contributions. The first contribution is to provide a geometric understanding. It is argued that the test statistic with minimum variance for exchangeable distributional deviations can serve as a good general-purpose test. The second contribution is to propose better omnibus tests, called circularly symmetric tests and obtained by circularizing reweighted Anderson-Darling test statistics or, more generally, test statistics based on the observed order statistics. The resulting tests are called circularized tests. A limited but arguably convincing simulation study on finite-sample performance demonstrates that circularized tests have good performance, as they typically outperform their parent methods in the simulation study. The third contribution is to establish new large-sample results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
