Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications
Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications
复制标题
DOI：
10.1214/20-sts784
复制
发表时间：
2021-05-01
影响因子：
5.7
通讯作者：
Yuan, Hongsong
中科院分区：
文献类型：
--
作者：
Lai, Tze Leung;Yuan, Hongsong
随机逼近于 1951 年引入，为当时新兴的统计学领域中的回归函数求根和优化提供了新的理论框架。这篇综述展示了它是如何随着统计学的其他发展（特别是时间序列和序贯分析）以及人工智能、经济学和工程学的应用而演变的。它在大数据时代的复兴导致了统计学和数据科学这一缩影的理论和应用的新进展。
Stochastic approximation was introduced in 1951 to provide a new theoretical framework for root finding and optimization of a regression function in the then-nascent field of statistics. This review shows how it has evolved in response to other developments in statistics, notably time series and sequential analysis, and to applications in artificial intelligence, economics and engineering. Its resurgence in the big data era has led to new advances in both theory and applications of this microcosm of statistics and data science.