论文标题
调查抽样中设计统计学的统计学习
Design-unbiased statistical learning in survey sampling
论文作者
论文摘要
设计一致的模型辅助估计已成为调查采样的标准实践。但是，到目前为止，缺乏一般理论，这使人们能够合并现代的机器学习技术，这可能会导致潜在的强大辅助模型。我们提出了一种子采样rao-blackwell方法，并在线性或非线性预测模型的帮助下开发了一个统计学习理论，以确切地设计依赖设计的估计。我们的方法利用了统计科学的经典思想以及机器学习的快速发展领域。提供了丰富的辅助信息，它可以比标准线性模型辅助方法产生可观的效率提高，同时确保对给定目标人群的有效估计，这是对在个体级别上对辅助模型的潜在误解的强大估计。
Design-consistent model-assisted estimation has become the standard practice in survey sampling. However, a general theory is lacking so far, which allows one to incorporate modern machine-learning techniques that can lead to potentially much more powerful assisting models. We propose a subsampling Rao-Blackwell method, and develop a statistical learning theory for exactly design-unbiased estimation with the help of linear or non-linear prediction models. Our approach makes use of classic ideas from Statistical Science as well as the rapidly growing field of Machine Learning. Provided rich auxiliary information, it can yield considerable efficiency gains over standard linear model-assisted methods, while ensuring valid estimation for the given target population, which is robust against potential mis-specifications of the assisting model at the individual level.
