{"title":"最近邻熵估计器的减偏","authors":"A. Kaltchenko, N. Timofeeva, E. A. Timofeev","doi":"10.1142/S0218127408022731","DOIUrl":null,"url":null,"abstract":"A new family of entropy estimators, constructed as a linear combination (weighted average) of nearest neighbor estimators with slightly different individual properties, is proposed. It is shown that a special sub-optimal selection of the coefficients in the linear combination results in a reduction of the estimatorpsilas bias. Computer simulation results are provided.","PeriodicalId":295946,"journal":{"name":"2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Bias reduction of the nearest neighbor entropy estimator\",\"authors\":\"A. Kaltchenko, N. Timofeeva, E. A. Timofeev\",\"doi\":\"10.1142/S0218127408022731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new family of entropy estimators, constructed as a linear combination (weighted average) of nearest neighbor estimators with slightly different individual properties, is proposed. It is shown that a special sub-optimal selection of the coefficients in the linear combination results in a reduction of the estimatorpsilas bias. Computer simulation results are provided.\",\"PeriodicalId\":295946,\"journal\":{\"name\":\"2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1142/S0218127408022731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/S0218127408022731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bias reduction of the nearest neighbor entropy estimator
A new family of entropy estimators, constructed as a linear combination (weighted average) of nearest neighbor estimators with slightly different individual properties, is proposed. It is shown that a special sub-optimal selection of the coefficients in the linear combination results in a reduction of the estimatorpsilas bias. Computer simulation results are provided.