What are the most important statistical ideas of the past 50 years?
arXiv:2012.00174
Abstract
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.
References in corpus (5)
- Deep Learning in Neural Networks: An Overview
- Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
- Interpretable machine learning: definitions, methods, and applications
- Expectation Propagation for approximate Bayesian inference
- Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure