{"title":"非参数回归鲁棒估计的比较","authors":"Ali Fadhil Abduljabbar, Afrah Mohammed Kadhim","doi":"10.25130/tjps.v28i1.1271","DOIUrl":null,"url":null,"abstract":"In order to get rid of or reduce the abnormal values of some phenomena that may be the reason for not obtaining the desired results. This makes us to get conclusions far from reality for the phenomenon we are studying. That the traditional nonparametric estimators are very sensitive to anomalous values, which prompted us to use the fortified estimators because they are not much affected by the anomalous values, as well as the nonparametric regression because it does not depend on the previous determinants or assumptions, but it depends directly and fundamentally on the data.","PeriodicalId":23142,"journal":{"name":"Tikrit Journal of Pure Science","volume":"61 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison the Robust Estimators Nonparametric of Nonparametric Regressions\",\"authors\":\"Ali Fadhil Abduljabbar, Afrah Mohammed Kadhim\",\"doi\":\"10.25130/tjps.v28i1.1271\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to get rid of or reduce the abnormal values of some phenomena that may be the reason for not obtaining the desired results. This makes us to get conclusions far from reality for the phenomenon we are studying. That the traditional nonparametric estimators are very sensitive to anomalous values, which prompted us to use the fortified estimators because they are not much affected by the anomalous values, as well as the nonparametric regression because it does not depend on the previous determinants or assumptions, but it depends directly and fundamentally on the data.\",\"PeriodicalId\":23142,\"journal\":{\"name\":\"Tikrit Journal of Pure Science\",\"volume\":\"61 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tikrit Journal of Pure Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25130/tjps.v28i1.1271\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tikrit Journal of Pure Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25130/tjps.v28i1.1271","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Comparison the Robust Estimators Nonparametric of Nonparametric Regressions
In order to get rid of or reduce the abnormal values of some phenomena that may be the reason for not obtaining the desired results. This makes us to get conclusions far from reality for the phenomenon we are studying. That the traditional nonparametric estimators are very sensitive to anomalous values, which prompted us to use the fortified estimators because they are not much affected by the anomalous values, as well as the nonparametric regression because it does not depend on the previous determinants or assumptions, but it depends directly and fundamentally on the data.