Since its invention, Efron’s bootstrap resampling approach has changed all the aspects of statistical inference, which has become the default framework whenever the classical inference approaches are not feasible. This paper introduces a new, more accurate, and efficient resampling approach, namely, the ranked simulated resampling approach. We show that, analytically and computationally, it is more efficient and precise than Efron’s uniform bootstrap resampling approach. We provide simulation studies and real data applications to support the comparison between the ranked simulated resampling approach and Efron’s uniform bootstrap resampling approach.

自发明以来，Efron 的 bootstrap 重采样方法改变了统计推理的所有方面，当经典推理方法不可行时，它已成为默认框架。本文介绍了一种新的、更准确、更高效的重采样方法，即排序模拟重采样方法。我们表明，在分析和计算上，它比 Efron 的统一引导重采样方法更有效和更精确。我们提供模拟研究和真实数据应用，以支持排序模拟重采样方法与 Efron 的统一引导重采样方法之间的比较。