{"title":"讨论:“分布式统计推断综述”","authors":"Shaogao Lv, Xingcai Zhou","doi":"10.1080/24754269.2021.2015868","DOIUrl":null,"url":null,"abstract":"First of all, we would like to congratulate Dr Gao et al. for their excellent paper, which provides a comprehensive overview of amounts of existing work on distributed estimation (learning). Different from related work Gu et al. (2019); Liu et al. (2021); Verbraeken et al. (2020) that focus on computing, storage and communication architecture, the current paper leverages how to guarantee statistical efficiency of a given distributed method from a statistical viewpoint. In the following, we divide our discussion into three parts:","PeriodicalId":22070,"journal":{"name":"Statistical Theory and Related Fields","volume":"6 1","pages":"105 - 107"},"PeriodicalIF":0.7000,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discussion of: ‘A review of distributed statistical inference’\",\"authors\":\"Shaogao Lv, Xingcai Zhou\",\"doi\":\"10.1080/24754269.2021.2015868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"First of all, we would like to congratulate Dr Gao et al. for their excellent paper, which provides a comprehensive overview of amounts of existing work on distributed estimation (learning). Different from related work Gu et al. (2019); Liu et al. (2021); Verbraeken et al. (2020) that focus on computing, storage and communication architecture, the current paper leverages how to guarantee statistical efficiency of a given distributed method from a statistical viewpoint. In the following, we divide our discussion into three parts:\",\"PeriodicalId\":22070,\"journal\":{\"name\":\"Statistical Theory and Related Fields\",\"volume\":\"6 1\",\"pages\":\"105 - 107\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2021-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Theory and Related Fields\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1080/24754269.2021.2015868\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Theory and Related Fields","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1080/24754269.2021.2015868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Discussion of: ‘A review of distributed statistical inference’
First of all, we would like to congratulate Dr Gao et al. for their excellent paper, which provides a comprehensive overview of amounts of existing work on distributed estimation (learning). Different from related work Gu et al. (2019); Liu et al. (2021); Verbraeken et al. (2020) that focus on computing, storage and communication architecture, the current paper leverages how to guarantee statistical efficiency of a given distributed method from a statistical viewpoint. In the following, we divide our discussion into three parts: