{"title":"主成分回归的主问题","authors":"H. Artigue, Gary Smith","doi":"10.1080/25742558.2019.1622190","DOIUrl":null,"url":null,"abstract":"Abstract Principal components regression (PCR) reduces a large number of explanatory variables in a regression model down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.","PeriodicalId":92618,"journal":{"name":"Cogent mathematics & statistics","volume":" ","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/25742558.2019.1622190","citationCount":"27","resultStr":"{\"title\":\"The principal problem with principal components regression\",\"authors\":\"H. Artigue, Gary Smith\",\"doi\":\"10.1080/25742558.2019.1622190\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Principal components regression (PCR) reduces a large number of explanatory variables in a regression model down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.\",\"PeriodicalId\":92618,\"journal\":{\"name\":\"Cogent mathematics & statistics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/25742558.2019.1622190\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cogent mathematics & statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/25742558.2019.1622190\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cogent mathematics & statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/25742558.2019.1622190","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS","Score":null,"Total":0}
The principal problem with principal components regression
Abstract Principal components regression (PCR) reduces a large number of explanatory variables in a regression model down to a small number of principal components. PCR is thought to be more useful, the more numerous the potential explanatory variables. The reality is that a large number of candidate explanatory variables does not make PCR more valuable; instead, it magnifies the failings of PCR.
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