Abstract In sequential analysis, an experimenter gathers information regarding an unknown functional (parameter) by observing random samples in successive steps. We discuss a number of distribution-free scenarios under a variety of loss functions. The number of observations gathered upon termination is a positive integer-valued random variable, customarily denoted by N. Often, a standardized version of N would follow an approximate normal distribution in the asymptotic sense. We provide exploratory data analysis (EDA) with the help of a number of interesting illustrations. We do so via large-scale simulation studies to demonstrate broad applicability of the purely sequential methodologies along with the appropriateness of asymptotic normality of the standardized stopping variables as a practical and useful guideline.

中文翻译：

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EDA 关于标准化顺序停止时间的渐近正态性，第二部分：无分布模型

摘要 在顺序分析中，实验者通过观察连续步骤中的随机样本来收集有关未知函数（参数）的信息。我们讨论了各种损失函数下的许多无分布场景。终止时收集到的观察数量是一个正整数值随机变量，通常用 N 表示。通常，N 的标准化版本将遵循渐近意义上的近似正态分布。我们借助许多有趣的插图提供探索性数据分析 (EDA)。我们通过大规模模拟研究来证明纯顺序方法的广泛适用性以及标准化停止变量的渐近正态性的适当性，作为实用和有用的指导方针。