{"title":"充分降维的推进","authors":"Weiqiang Hang, Yingcun Xia","doi":"10.1002/wics.1516","DOIUrl":null,"url":null,"abstract":"The sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and forward regression methods. In this survey, we briefly discuss advances of methods and present problems that needs further investigation in the second group.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2020-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1516","citationCount":"0","resultStr":"{\"title\":\"Advance of the sufficient dimension reduction\",\"authors\":\"Weiqiang Hang, Yingcun Xia\",\"doi\":\"10.1002/wics.1516\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and forward regression methods. In this survey, we briefly discuss advances of methods and present problems that needs further investigation in the second group.\",\"PeriodicalId\":47779,\"journal\":{\"name\":\"Wiley Interdisciplinary Reviews-Computational Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2020-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1002/wics.1516\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Wiley Interdisciplinary Reviews-Computational Statistics\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/wics.1516\",\"RegionNum\":2,\"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":"Wiley Interdisciplinary Reviews-Computational Statistics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/wics.1516","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
The sufficient dimension reduction of Li has been seen a steady development in the past 30 years in both methodology and application. The main approaches can be categorized into two groups: The inverse regression methods and forward regression methods. In this survey, we briefly discuss advances of methods and present problems that needs further investigation in the second group.
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