{"title":"误差密度的非参数核估计","authors":"Z. Li, Shu Zhao Zou","doi":"10.1109/WITS.1994.513920","DOIUrl":null,"url":null,"abstract":"Summary form only given. Consider a linear model, y/sub i/=x'/sub i//spl beta/+e/sub i/, i=1,2,..., x'/sub i/s are p(/spl ges/1) dimension known vectors and /spl beta/(/spl isin/R/spl deg/) is an unknown parametric vector and e/sub i/ are assumed i.i.d.r.v.'s from a common unknown density function f(x) with med (e/sub i/)=0. Based on LAD (least absolute deviations) estimator /spl beta//spl tilde/ of /spl beta/, we propose a nonparametric method to estimate unknown f(x). A kernel estimator f/spl tilde//sub n/(x) is obtained. Large sample properties of f/spl tilde//sub n/(x) are studied. Some computational examples are also given.","PeriodicalId":423518,"journal":{"name":"Proceedings of 1994 Workshop on Information Theory and Statistics","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Nonparametric kernel estimation for error density\",\"authors\":\"Z. Li, Shu Zhao Zou\",\"doi\":\"10.1109/WITS.1994.513920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Summary form only given. Consider a linear model, y/sub i/=x'/sub i//spl beta/+e/sub i/, i=1,2,..., x'/sub i/s are p(/spl ges/1) dimension known vectors and /spl beta/(/spl isin/R/spl deg/) is an unknown parametric vector and e/sub i/ are assumed i.i.d.r.v.'s from a common unknown density function f(x) with med (e/sub i/)=0. Based on LAD (least absolute deviations) estimator /spl beta//spl tilde/ of /spl beta/, we propose a nonparametric method to estimate unknown f(x). A kernel estimator f/spl tilde//sub n/(x) is obtained. Large sample properties of f/spl tilde//sub n/(x) are studied. Some computational examples are also given.\",\"PeriodicalId\":423518,\"journal\":{\"name\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"volume\":\"95 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-10-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 Workshop on Information Theory and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WITS.1994.513920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 Workshop on Information Theory and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WITS.1994.513920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Summary form only given. Consider a linear model, y/sub i/=x'/sub i//spl beta/+e/sub i/, i=1,2,..., x'/sub i/s are p(/spl ges/1) dimension known vectors and /spl beta/(/spl isin/R/spl deg/) is an unknown parametric vector and e/sub i/ are assumed i.i.d.r.v.'s from a common unknown density function f(x) with med (e/sub i/)=0. Based on LAD (least absolute deviations) estimator /spl beta//spl tilde/ of /spl beta/, we propose a nonparametric method to estimate unknown f(x). A kernel estimator f/spl tilde//sub n/(x) is obtained. Large sample properties of f/spl tilde//sub n/(x) are studied. Some computational examples are also given.
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