{"title":"一些Hurst参数估计的比较","authors":"C. Stolojescu, A. Isar","doi":"10.1109/OPTIM.2012.6231802","DOIUrl":null,"url":null,"abstract":"In the last few years the long-range dependence analysis of time-series became more important. A key parameter characterizing long-range dependent processes is the Hurst parameter H. The goal of this paper is to compare some estimation techniques for the Hurst parameter. We found that the best estimator is the one based on the second order discrete wavelet transform statistical analysis and works for second order wide sense stationary random processes.","PeriodicalId":382406,"journal":{"name":"2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A comparison of some Hurst parameter estimators\",\"authors\":\"C. Stolojescu, A. Isar\",\"doi\":\"10.1109/OPTIM.2012.6231802\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last few years the long-range dependence analysis of time-series became more important. A key parameter characterizing long-range dependent processes is the Hurst parameter H. The goal of this paper is to compare some estimation techniques for the Hurst parameter. We found that the best estimator is the one based on the second order discrete wavelet transform statistical analysis and works for second order wide sense stationary random processes.\",\"PeriodicalId\":382406,\"journal\":{\"name\":\"2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OPTIM.2012.6231802\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OPTIM.2012.6231802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In the last few years the long-range dependence analysis of time-series became more important. A key parameter characterizing long-range dependent processes is the Hurst parameter H. The goal of this paper is to compare some estimation techniques for the Hurst parameter. We found that the best estimator is the one based on the second order discrete wavelet transform statistical analysis and works for second order wide sense stationary random processes.