What are the Most Important Statistical Ideas of the Past 50 Years?

• 成果类型: Article
• 署名作者: Gelman, Andrew; Vehtari, Aki
• 署名单位: Columbia University; Aalto University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1938081
• 发表日期: 2021
• 页码: 2087-2097
• 关键词: empirical bayes estimators; maximum-likelihood; limiting risk; of-fit; confidence; inference; models; soft; exchangeability; distributions

摘要

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.