AI 中文总结
本文探究五折交叉拟合对结合机器学习方法的调查估计无响应调整的改进效果，通过含90种配置的蒙特卡洛模拟，以逻辑回归为基准，对比随机森林、梯度提升机的表现。
AI 中文摘要
本文研究当使用灵活的机器学习方法估计响应倾向时，五折交叉拟合是否能改进调查估计中的无响应调整。我们开展有限总体蒙特卡洛模拟，设置90种实验配置，每种配置重复2000次，变化样本量、响应率和响应机制结构。采用逻辑回归作为传统参数基准，同时考虑随机森林（Random Forest）和梯度提升机（Gradient Boosting Machine）作为灵活的非参数模型。
英文摘要
This paper investigates whether five fold cross fitting improves nonresponse adjustment in survey estimation when flexible machine learning methods are used to estimate response propensities. We conduct a finite population Monte Carlo simulation with 90 experimental configurations and 2,000 replications per configuration, varying sample size, response rate, and the structure of the response mechanism. Logistic regression is used as a conventional parametric benchmark, while Random Forest and Gradient Boosting Machine are considered as flexible nonparametric models.
