{"title":"熔炉","authors":"R. Tibshirani, T. Hastie","doi":"10.1353/obs.2021.0012","DOIUrl":null,"url":null,"abstract":"Abstract:Leo Breiman's article \"Statistical Modeling: The two cultures\" was timely and provocative. He advocated for Statisticians to learn about and appreciate a different \"culture\": an algorithmic approach, as distinct from the familiar, stochastic, data modeling approach to Statistics. While we have appreciated and contributed to the algorithmic approach, we have always had a foot in both camps. Here we advocate for a \"melting pot\", arguing that both approaches have their virtues, sometimes on the same problem.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1353/obs.2021.0012","citationCount":"0","resultStr":"{\"title\":\"A Melting Pot\",\"authors\":\"R. Tibshirani, T. Hastie\",\"doi\":\"10.1353/obs.2021.0012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract:Leo Breiman's article \\\"Statistical Modeling: The two cultures\\\" was timely and provocative. He advocated for Statisticians to learn about and appreciate a different \\\"culture\\\": an algorithmic approach, as distinct from the familiar, stochastic, data modeling approach to Statistics. While we have appreciated and contributed to the algorithmic approach, we have always had a foot in both camps. Here we advocate for a \\\"melting pot\\\", arguing that both approaches have their virtues, sometimes on the same problem.\",\"PeriodicalId\":74335,\"journal\":{\"name\":\"Observational studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1353/obs.2021.0012\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Observational studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1353/obs.2021.0012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2021.0012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Abstract:Leo Breiman's article "Statistical Modeling: The two cultures" was timely and provocative. He advocated for Statisticians to learn about and appreciate a different "culture": an algorithmic approach, as distinct from the familiar, stochastic, data modeling approach to Statistics. While we have appreciated and contributed to the algorithmic approach, we have always had a foot in both camps. Here we advocate for a "melting pot", arguing that both approaches have their virtues, sometimes on the same problem.
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