Volume 1,Issue 3
Fall 2025
共形预测框架下的预测区间估计方法及乳腺癌应用研究
共形预测是一类具有有限样本保证的统计方法，能够在无需依赖模型分布假设的前提下实现有效的不确定性量化。本
文系统研究了完全共形预测的基本原理与算法框架，并重点分析了几种典型变体，包括分裂共形预测、交叉共形预测
和Jackknife方法，探讨其在提升预测效率和覆盖精度方面的优势。通过对回归问题的数值模拟与波士顿房价数据的
实证分析，比较各方法在预测区间宽度、覆盖率和计算效率上的表现。进一步将共形预测方法应用于乳腺癌风险预测
中，结合主成分分析与核密度估计分类器，实现了预测区域的动态更新。结果表明，共形预测能够有效表征分类模型
的不确定性，尤其在处理复杂数据时展现出良好的稳定性与适应性。
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