{"title":"非参数统计","authors":"Aliakbar Montazer Haghighi, Indika Wickramasinghe","doi":"10.1201/9781351238403-6","DOIUrl":null,"url":null,"abstract":"Main themes Main themes The topics treated during this course are : 1. Nonparametric estimation of a distribution function 2. Nonparametric estimation of a density function : the kernel method 3. Nonparametric estimation of a regression function : kernel estimation local polynomial estimation spline estimation The material will essentially be treated from an applied point of view of methodology. The student will study software applications of the proposed methods.","PeriodicalId":231704,"journal":{"name":"Probability, Statistics, and Stochastic Processes for Engineers and Scientists","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonparametric Statistics\",\"authors\":\"Aliakbar Montazer Haghighi, Indika Wickramasinghe\",\"doi\":\"10.1201/9781351238403-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Main themes Main themes The topics treated during this course are : 1. Nonparametric estimation of a distribution function 2. Nonparametric estimation of a density function : the kernel method 3. Nonparametric estimation of a regression function : kernel estimation local polynomial estimation spline estimation The material will essentially be treated from an applied point of view of methodology. The student will study software applications of the proposed methods.\",\"PeriodicalId\":231704,\"journal\":{\"name\":\"Probability, Statistics, and Stochastic Processes for Engineers and Scientists\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Probability, Statistics, and Stochastic Processes for Engineers and Scientists\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1201/9781351238403-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Probability, Statistics, and Stochastic Processes for Engineers and Scientists","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1201/9781351238403-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Main themes Main themes The topics treated during this course are : 1. Nonparametric estimation of a distribution function 2. Nonparametric estimation of a density function : the kernel method 3. Nonparametric estimation of a regression function : kernel estimation local polynomial estimation spline estimation The material will essentially be treated from an applied point of view of methodology. The student will study software applications of the proposed methods.
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