边界穿越问题中的最优多级抽样
Optimal Multistage Sampling in a Boundary-Crossing Problem
摘要：
具有已知正漂移的布朗运动被分阶段采样，直到它穿过正边界$a$。一个家庭的多级采样器，通过改变在每个阶段的阶段大小的边界上的控制预期的过冲被证明是最佳的大$a$，最大限度地减少了线性组合的过冲和级数。应用假设检验进行了讨论。
英文摘要：
Brownian motion with known positive drift is sampled in stages until it crosses a positive boundary $a$. A family of multistage samplers that control the expected overshoot over the boundary by varying the stage size at each stage is shown to be optimal for large $a$, minimizing a linear combination of overshoot and number of stages. Applications to hypothesis testing are discussed.
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。
