{"title":"基于改进Akaike信息准则的半参数和加性模型选择","authors":"J. Simonoff, Chih-Ling Tsai","doi":"10.1080/10618600.1999.10474799","DOIUrl":null,"url":null,"abstract":"Abstract An improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.","PeriodicalId":309676,"journal":{"name":"NYU: IOMS: Statistics Working Papers (Topic)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"49","resultStr":"{\"title\":\"Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion\",\"authors\":\"J. Simonoff, Chih-Ling Tsai\",\"doi\":\"10.1080/10618600.1999.10474799\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract An improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.\",\"PeriodicalId\":309676,\"journal\":{\"name\":\"NYU: IOMS: Statistics Working Papers (Topic)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NYU: IOMS: Statistics Working Papers (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/10618600.1999.10474799\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NYU: IOMS: Statistics Working Papers (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10618600.1999.10474799","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Semiparametric and Additive Model Selection Using an Improved Akaike Information Criterion
Abstract An improved AIC-based criterion is derived for model selection in general smoothing-based modeling, including semiparametric models and additive models. Examples are provided of applications to goodness-of-fit, smoothing parameter and variable selection in an additive model and semiparametric models, and variable selection in a model with a nonlinear function of linear terms.
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