{"title":"危险率函数及其一阶导数和二阶导数的光滑核估计","authors":"I. Fuks, G. Koshkin","doi":"10.1109/DT.2016.7557164","DOIUrl":null,"url":null,"abstract":"A class of nonparametric kernel estimators is suggested for an unknown hazard rate function and its derivatives. Both weak and mean square convergence of the proposed estimators to the unknown hazard function and its derivatives are proved. These estimators can be used for solving the problems of operational reliability of complex physical, technical, and program systems under uncertainty conditions.","PeriodicalId":281446,"journal":{"name":"2016 International Conference on Information and Digital Technologies (IDT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smooth kernel estimators of the hazard rate function and its first and second derivatives\",\"authors\":\"I. Fuks, G. Koshkin\",\"doi\":\"10.1109/DT.2016.7557164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A class of nonparametric kernel estimators is suggested for an unknown hazard rate function and its derivatives. Both weak and mean square convergence of the proposed estimators to the unknown hazard function and its derivatives are proved. These estimators can be used for solving the problems of operational reliability of complex physical, technical, and program systems under uncertainty conditions.\",\"PeriodicalId\":281446,\"journal\":{\"name\":\"2016 International Conference on Information and Digital Technologies (IDT)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Information and Digital Technologies (IDT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DT.2016.7557164\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Information and Digital Technologies (IDT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DT.2016.7557164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smooth kernel estimators of the hazard rate function and its first and second derivatives
A class of nonparametric kernel estimators is suggested for an unknown hazard rate function and its derivatives. Both weak and mean square convergence of the proposed estimators to the unknown hazard function and its derivatives are proved. These estimators can be used for solving the problems of operational reliability of complex physical, technical, and program systems under uncertainty conditions.
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