E-变量和分布类的随机性检验
E-variables and tests of randomness for distribution classes
AI总结：
本文提出了一种基于e-变量近似性的方法，用于构造针对分布类的显式随机性测试。
摘要：
E-变量是一种相对较新的用于检验统计假设的方法，近年来经历了重大发展。本文介绍了e-变量近似性方法，并利用该方法开发了一种通用近似技术，使我们能够为应用中重要的流行分布类构造e-变量。E-变量最初基于算法信息理论中Levin提出的（平均有界）随机性测试概念。我们展示我们的e-变量构造可以用于针对分布类提供显式的随机性测试构造。
英文摘要：
E-variables are a relatively new approach for testing statistical hypotheses that has been experiencing major development during the last several years. In this paper we introduce the method of e-variable-approximability and use it to develop a general approximation technique allowing us to construct e-variables for popular distribution classes important for applications. E-variables were originally based on a concept of Levin's (average-bounded) randomness tests from Algorithmic Information Theory. We show that our construction of e-variables can be used to provide an explicit construction for a randomness test with respect to a class of distributions.
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。
