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English(EN) Deep Neural Networks for Doubly Robust Estimation with Nonprobability Survey Samples

深度神经网络通过结合数据源增强调查估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的框架，使用深度神经网络（DNNs）来结合概率和非概率调查样本，以实现更稳健的估计。该方法将非概率样本的抽样得分建模为一个未知的函数，通过最大化整合来自概率和非概率来源数据的伪似然来估计。该方法旨在提高对选择机制误设的稳健性，尤其是在选择机制是非线性时，并通过模拟研究和真实世界数据进行了评估。 AI

影响 这项研究引入了一种新颖的深度学习方法，以提高从组合调查数据源得出的统计估计的准确性和稳健性。

排序理由 该集群包含一篇详细介绍新统计方法的学术论文。

在 arXiv stat.ML 阅读 →

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深度神经网络通过结合数据源增强调查估计

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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yufang Dai, Shihua Luo, Wendy Lou, Zilin Wang, Xuewen Lu ·

    用于非概率抽样调查样本的深度神经网络进行双重稳健估计

    arXiv:2605.28762v1 Announce Type: cross Abstract: Integrating probability and nonprobability survey samples is an important problem in modern survey sampling. Nonprobability samples often contain rich outcome information but may lack population representativeness, whereas probabi…

  2. arXiv stat.ML TIER_1 English(EN) · Xuewen Lu ·

    用于非概率抽样调查样本的加倍稳健估计的深度神经网络

    Integrating probability and nonprobability survey samples is an important problem in modern survey sampling. Nonprobability samples often contain rich outcome information but may lack population representativeness, whereas probability samples provide design-based auxiliary inform…

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