{"title":"Rejoinder: Sparse Regression: Scalable Algorithms and Empirical Performance","authors":"D. Bertsimas, J. Pauphilet, Bart P. G. Van Parys","doi":"10.1214/20-sts701rej","DOIUrl":null,"url":null,"abstract":"their","PeriodicalId":51172,"journal":{"name":"Statistical Science","volume":" ","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2020-11-01","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-sts701rej","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
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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