{"title":"半参数加性回归","authors":"J. Cuzick","doi":"10.1111/J.2517-6161.1992.TB01455.X","DOIUrl":null,"url":null,"abstract":"A simple estimator for β is proposed for the model y=x'β+g(1)+error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a semiparametric likelihood and in the simplest case reduces to minimizing the distance between the pseudoresiduals y-x'β and a local linear cross-validated estimate of them. When the errors are independent with finite variance, the bias and variance of the estimate are computed and compared against the least squares estimate with g known","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1992-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"91","resultStr":"{\"title\":\"Semiparametric additive regression\",\"authors\":\"J. Cuzick\",\"doi\":\"10.1111/J.2517-6161.1992.TB01455.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A simple estimator for β is proposed for the model y=x'β+g(1)+error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a semiparametric likelihood and in the simplest case reduces to minimizing the distance between the pseudoresiduals y-x'β and a local linear cross-validated estimate of them. When the errors are independent with finite variance, the bias and variance of the estimate are computed and compared against the least squares estimate with g known\",\"PeriodicalId\":17425,\"journal\":{\"name\":\"Journal of the royal statistical society series b-methodological\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"91\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the royal statistical society series b-methodological\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2517-6161.1992.TB01455.X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the royal statistical society series b-methodological","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2517-6161.1992.TB01455.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A simple estimator for β is proposed for the model y=x'β+g(1)+error, g smooth but unknown. The approach is to approximate the estimating equation obtained from a semiparametric likelihood and in the simplest case reduces to minimizing the distance between the pseudoresiduals y-x'β and a local linear cross-validated estimate of them. When the errors are independent with finite variance, the bias and variance of the estimate are computed and compared against the least squares estimate with g known
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