Abstract
We argue that the most important statistical ideas of the past half century
are: counterfactual causal inference, bootstrapping and simulation-based
inference, overparameterized models and regularization, multilevel models,
generic computation algorithms, adaptive decision analysis, robust inference,
and exploratory data analysis. We discuss common features of these ideas, 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.
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