{"title":"随机逼近:从统计起源到大数据，多学科应用","authors":"T. Lai, Hongsong Yuan","doi":"10.1214/20-STS784","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications\",\"authors\":\"T. Lai, Hongsong Yuan\",\"doi\":\"10.1214/20-STS784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":51172,\"journal\":{\"name\":\"Statistical Science\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Science\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1214/20-STS784\",\"RegionNum\":1,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Science","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1214/20-STS784","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Stochastic Approximation: From Statistical Origin to Big-Data, Multidisciplinary Applications
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.
期刊介绍：
The central purpose of Statistical Science is to convey the richness, breadth and unity of the field by presenting the full range of contemporary statistical thought at a moderate technical level, accessible to the wide community of practitioners, researchers and students of statistics and probability.
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