Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage. Conformal methods are an active research topic in statistics and machine learning, but only recently have they been extended to non-exchangeable data. In this paper, we invite survey methodologists to begin using and contributing to conformal methods. We introduce how conformal prediction can be applied to data from several common complex sample survey designs, under a framework of design-based inference for a finite population, and we point out gaps where survey methodologists could fruitfully apply their expertise. Our simulations empirically bear out the theoretical guarantees of finite-sample coverage, and our real-data example demonstrates how conformal prediction can be applied to complex sample survey data in practice.
翻译:非正式的预测是一种假设和假设,可以对几乎任意的预测模型产生无分发的预测间隔或套件,有保证的有限抽样覆盖面; 非正式的方法是统计和机器学习方面的一个积极研究课题,但直到最近才扩大到非交换数据; 在本文中,我们邀请调查方法学家开始使用和帮助采用符合的方法; 我们介绍如何在基于设计对有限人口进行推断的框架内,将一致的预测应用于若干共同的复杂抽样调查设计中的数据; 我们指出调查方法学家能够卓有成效地运用其专门知识的差距。 我们的模拟经验证明了有限抽样调查的理论保障,我们的真实数据实例表明如何在实践中将一致的预测应用于复杂的抽样调查数据。</s>