{"title":"非高斯噪声存在下的参数估计","authors":"H. Salzwedel","doi":"10.1109/CDC.1980.271885","DOIUrl":null,"url":null,"abstract":"A method for parameter estimation is derived that is insensitive to the noise distribution, and an example of its use for nonlinear systems is given. The method combines the sensitivity of the maximum-likelihood parameter estimator with the robustness of order statistics to reduce estimation uncertainty significantly, with only a slight increase in the variance. This algorithm shows improvements over conventional parameter estimates, in particular, in the case of small data sets.","PeriodicalId":332964,"journal":{"name":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1980-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Parameter estimation in the presence of non-Gaussian noise\",\"authors\":\"H. Salzwedel\",\"doi\":\"10.1109/CDC.1980.271885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A method for parameter estimation is derived that is insensitive to the noise distribution, and an example of its use for nonlinear systems is given. The method combines the sensitivity of the maximum-likelihood parameter estimator with the robustness of order statistics to reduce estimation uncertainty significantly, with only a slight increase in the variance. This algorithm shows improvements over conventional parameter estimates, in particular, in the case of small data sets.\",\"PeriodicalId\":332964,\"journal\":{\"name\":\"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes\",\"volume\":\"9 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1980-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.1980.271885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.1980.271885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parameter estimation in the presence of non-Gaussian noise
A method for parameter estimation is derived that is insensitive to the noise distribution, and an example of its use for nonlinear systems is given. The method combines the sensitivity of the maximum-likelihood parameter estimator with the robustness of order statistics to reduce estimation uncertainty significantly, with only a slight increase in the variance. This algorithm shows improvements over conventional parameter estimates, in particular, in the case of small data sets.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
