论文标题
过去50年中最重要的统计思想是什么？
What are the most important statistical ideas of the past 50 years?
论文作者
论文摘要
我们回顾了过去半个世纪最重要的统计思想，我们将其归类为：反事实因果推理，基于自举的推理和基于模拟的推理，过度参数化模型和正则化，贝叶斯多级模型，通用计算算法，适应性决策分析，强大的推论，强大的推论和探索数据分析。我们讨论了这些子领域的关键贡献，它们与现代计算和大数据的关系以及未来几十年中如何开发和扩展。本文的目的是激发有关统计和数据科学研究的较大研究主题的思考和讨论。
We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss key contributions in these subfields, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this article is to provoke thought and discussion regarding the larger themes of research in statistics and data science.
