What are the Most Important Statistical Ideas of the Past 50 Years?
回顾了过去半个世纪最重要的统计思想，包括反事实因果推断、自助法、过参数化模型与正则化、贝叶斯多层模型等，并探讨其与计算和大数据的关系，适合对统计和数据科学历史与未来感兴趣的学者。
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.