通过重采样实现稳定性：实数轴之外的统计问题
Stability via resampling: statistical problems beyond the real line
AI总结：
本文提出一个有限样本理论结果，证明袋装法在一般输出空间（如向量或密度）中的稳定性，并通过合成及真实数据在因果推断、非参数回归和贝叶斯模型选择中验证其效果。
摘要：
基于重采样方法（如自助法或子采样）的模型平均技术已在统计学的许多领域得到应用，其明确目标往往是提升最终输出的稳定性。我们提供了一个通用的、有限样本的理论结果，保证当袋装法应用于在一般空间中返回输出的算法时（即输出不一定是实数值，例如估计权重向量或密度函数的算法）具有稳定性。我们在合成数据和真实数据上，针对一系列问题设置（包括因果推断、非参数回归和贝叶斯模型选择）实证评估了袋装法的稳定性。
英文摘要：
Model averaging techniques based on resampling methods (such as bootstrapping or subsampling) have been utilized across many areas of statistics, often with the explicit goal of promoting stability in the resulting output. We provide a general, finite-sample theoretical result guaranteeing the stability of bagging when applied to algorithms that return outputs in a general space, so that the output is not necessarily a real-valued -- for example, an algorithm that estimates a vector of weights or a density function. We empirically assess the stability of bagging on synthetic and real-world data for a range of problem settings, including causal inference, nonparametric regression, and Bayesian model selection.
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。
