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在多级回归和后分层 (MRP) 工作流程中使用留下一个异常交叉验证 (LOO):一个警告故事
Swen Kuh1,2, Lauren Kennedy1,2, Qixuan Chen3
1School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia.
Statistics in medicine
|December 26, 2023
概括
像PSIS-LOO这样的离开一个的交叉验证 (LOO) 方法可能无法可靠地评估多级回归和分层后 (MRP) 模型. 这些技术难以准确地对模型进行排名,特别是对于小面积估计,这表明在MRP验证的应用中应该谨慎.
科学领域:
- 统计 统计 统计 统计
- 计算统计学 计算统计学
- 调查方法 调查方法
背景情况:
- 多级回归和后分层 (MRP) 越来越多地用于人口推断.
- 在MRP中模型有效性至关重要,但研究不足,特别是在验证技术方面.
- 评估不同MRP模型的性能对于可靠的人口估计至关重要.
研究的目的:
- 评价留出一个缺点交叉验证 (LOO) 的实用性,用于在MRP中比较贝叶斯模型.
- 为了研究两个近似的LOO计算:帕雷托平滑重要性抽样 (PSIS-LOO) 和调查加权版本 (WTD-PSIS-LOO).
- 评估这些LOO标准对人口水平和小面积估计的模型排名的准确性.
主要方法:
- 使用两个模拟设计来测试PSIS-LOO和WTD-PSIS-LOO的性能.
- 检查了人口估计和小面积估计的模型排名准确性.
- 将这些方法应用于来自国家健康和营养检查调查 (NHANES) 的现实数据.
主要成果:
- 无论是PSIS-LOO还是WTD-PSIS-LOO都没有始终恢复对人口估计的正确模型顺序,尽管他们确定了最好的和最差的模型.
- 在不同的小区域中,模型性能有所不同,这使得小区域估计的验证变得复杂.
- 在考虑不同的先验时,模型排名在较小的区域水平上略有改善.
- 现实世界NHANES数据分析证实了模拟结果,表明对MRP的PSIS-LOO应该谨慎使用.
结论:
- 基于PSIS-LOO的模型验证可能不完全适合评估MRP方法,因为聚合效应.
- 在MRP中的聚合阶段可以掩盖个人级预测错误,影响LOO性能.
- 这些发现凸显了在MRP验证的背景下,需要仔细应用和可能重新评估LOO技术.
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