广义线性模型中混合置信序列的高效计算
Efficient computation of mixture confidence sequences in generalized linear models
- University of Verona（维罗纳大学）
- University of Udine（乌迪内大学）
- University of Padova（帕多瓦大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对序贯数据下经典置信区间易产生矛盾推断的问题，提出高效计算广义线性模型回归系数混合置信序列的策略，模拟与流式数据验证其有效性与实用性。
AI中文摘要：
经典置信区间在样本量不断累积的不同时间点重复获取时，会以高概率产生矛盾的推断。我们提出了一种简单高效的策略，用于在此序贯框架下计算广义线性模型中回归系数的混合置信序列。模拟实验证明了我们方法的计算便利性，以及当观测数据随时间分批到达时，基于这些任意时刻有效工具进行推断结论的重要性。所提方法在美国国家汽车采样系统的流式数据分析中也展示了其实用性。
英文摘要：
Classical confidence intervals, when repeatedly obtained on accumulating data at different sample sizes, produce contradictory inferences with high probability. We propose a simple and efficient strategy for computing, instead, mixture confidence sequences for regression coefficients in generalized linear models under this sequential framework. Simulations demonstrate the computational convenience of our approach and the importance of drawing inferential conclusions based on these anytime-valid tools when observations become available in batches over time. The usefulness of the proposed procedure is also shown in the analysis of streaming data from the American National Automotive Sampling System.
