Abstract For several decades, the resampling based bootstrap has been widely used for computing confidence intervals (CIs) for applications where no exact method is available. However, there are many applications where the resampling bootstrap method cannot be used. These include situations where the data are heavily censored due to the success response being a rare event, situations where there is insufficient mixing of successes and failures across the explanatory variable(s), and designed experiments where the number of parameters is close to the number of observations. These three situations all have in common that there may be a substantial proportion of the resamples where it is not possible to estimate all of the parameters in the model. This article reviews the fractional-random-weight bootstrap method and demonstrates how it can be used to avoid these problems and construct CIs in a way that is accessible to statistical practitioners. The fractional-random-weight bootstrap method is easy to use and has advantages over the resampling method in many challenging applications.

中文翻译：

---

分数随机权重引导程序的应用

摘要 几十年来，基于重采样的 bootstrap 已广泛用于计算置信区间 (CI)，适用于没有精确方法可用的应用。但是，有许多应用程序无法使用重采样引导方法。这些包括由于成功响应是罕见事件而严重审查数据的情况、解释变量中成功和失败的混合不足的情况以及参数数量接近于数字的设计实验的观察。这三种情况都有一个共同点，即可能有很大比例的重采样无法估计模型中的所有参数。本文回顾了分数随机权重 bootstrap 方法，并演示了如何使用它来避免这些问题并以统计从业人员可以访问的方式构建 CI。分数随机权重引导方法易于使用，并且在许多具有挑战性的应用中比重采样方法具有优势。