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WS Peng，YW Fang，RJ Zhan，YL Wu. "Two approximation algorithms of error spectrum for estimation performance evaluation". Optik - International Journal for Light and Electron Optics March 2016, Vol.127(5):2811–2821 发自小木虫Android客户端 |
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心静_依然
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peng_weishi: 金币+10, ★★★★★最佳答案, 谢谢 2016-03-10 21:05:12
lazy锦溪: LS-EPI+1, 感谢应助！ 2016-03-10 23:34:00
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peng_weishi: 金币+10, ★★★★★最佳答案, 谢谢 2016-03-10 21:05:12
lazy锦溪: LS-EPI+1, 感谢应助！ 2016-03-10 23:34:00
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Accession number: 20160902007039 Title: Two approximation algorithms of error spectrum for estimation performance evaluation Authors: Peng, Wei-Shi1, 2 Email author peng_weishi@163.com; Fang, Yang-Wang1 Email author ywfang2008@sohu.com; Zhan, Ren-Jun2 Email author zhanrenjun@aliyun.com; Wu, You-Li1 Email author wu_youli@126.com Author affiliation: 1 School of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an, Shaanxi, China 2 School of Equipment Engineering, Armed Police Force Engineering University, Xi'an, Shaanxi, China Corresponding author: Peng, Wei-Shi (peng_weishi@163.com) Source title: Optik Abbreviated source title: Optik Volume: 127 Issue: 5 Issue date: March 1, 2016 Publication year: 2016 Pages: 2811-2821 Language: English ISSN: 00304026 Document type: Journal article (JA) Publisher: Elsevier GmbH Abstract: Error spectrum is a comprehensive metric for evaluation of estimation performance in that it is an aggregation of many incomprehensive measures. However, error spectrum requires computing the expectation of the rth power of the estimation-error-norm as using it to evaluate an estimator's performance. Therefore unless the error distribution is given, it's usually not easy to obtain the error spectrum. To alleviate this difficulty, two approximation algorithms are proposed. One is the Gaussian mixture method, which calculated the error spectrum by capturing the probability density function. The other using the sample is the power means error method. Furthermore, how the Gaussian mixture method and power means error method can be used in estimation performance evaluation are analyzed not only in the large sample case but also in the small sample case. Numerical examples are provided to illustrate the effectiveness of the above two algorithms. It is shown that the two proposed algorithms can be applied easily to calculate the error spectrum in estimator performance evaluation. © 2015 Elsevier GmbH. All rights reserved. Number of references: 30 Main heading: Approximation algorithms Controlled terms: Algorithms - Errors - Estimation - Gaussian distribution - Probability density function Uncontrolled terms: Error distributions - Error spectrum - Estimation errors - Estimation performance - Gaussian mixture methods - Gaussian mixtures - Power means - Small sample case Classification code: 921 Mathematics - 922.1 Probability Theory DOI: 10.1016/j.ijleo.2015.11.204 Database: Compendex Compilation and indexing terms, © 2016 Elsevier Inc. |
2楼2016-03-10 16:55:50
心静_依然
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Two approximation algorithms of error spectrum for estimation performance evaluation 作者  eng, WS (Peng, Wei-Shi)[ 1,2 ] ; Fang, YW (Fang, Yang-Wang)[ 1 ] ; Zhan, RJ (Zhan, Ren-Jun)[ 2 ] ; Wu, YL (Wu, You-Li)[ 1 ]OPTIK 卷: 127 期: 5 页: 2811-2821 DOI: 10.1016/j.ijleo.2015.11.204 出版年: 2016 查看期刊信息 摘要 Error spectrum is a comprehensive metric for evaluation of estimation performance in that it is an aggregation of many incomprehensive measures. However, error spectrum requires computing the expectation of the rth power of the estimation-error-norm as using it to evaluate an estimator's performance. Therefore unless the error distribution is given, it's usually not easy to obtain the error spectrum. To alleviate this difficulty, two approximation algorithms are proposed. One is the Gaussian mixture method, which calculated the error spectrum by capturing the probability density function. The other using the sample is the power means error method. Furthermore, how the Gaussian mixture method and power means error method can be used in estimation performance evaluation are analyzed not only in the large sample case but also in the small sample case. Numerical examples are provided to illustrate the effectiveness of the above two algorithms. It is shown that the two proposed algorithms can be applied easily to calculate the error spectrum in estimator performance evaluation. (C) 2015 Elsevier GmbH. All rights reserved. 关键词 作者关键词:Error spectrum; Power means error; Gaussian mixture; Approximation algorithm; Estimation performance evaluation KeyWords Plus:MAXIMUM-LIKELIHOOD; EM ALGORITHM 作者信息 通讯作者地址: Peng, WS (通讯作者) Air Force Engn Univ, Sch Aeronaut & Astronaut Engn, Xian 710038, Shaanxi, Peoples R China. 通讯作者地址: Peng, WS (通讯作者) Armed Police Force Engn Univ, Sch Equipment Engn, Xian 710086, Shaanxi, Peoples R China. 地址: [ 1 ] Air Force Engn Univ, Sch Aeronaut & Astronaut Engn, Xian 710038, Shaanxi, Peoples R China [ 2 ] Armed Police Force Engn Univ, Sch Equipment Engn, Xian 710086, Shaanxi, Peoples R China 电子邮件地址:peng_weishi@163.com; ywfang2008@sohu.com; zhanrenjun@aliyun.com; wu_youli@126.com 基金资助致谢 基金资助机构 授权号 Province Natural Science Foundation of Shaanxi Province in China 2014JQ8339 查看基金资助信息 出版商 ELSEVIER GMBH, URBAN & FISCHER VERLAG, OFFICE JENA, P O BOX 100537, 07705 JENA, GERMANY 类别 / 分类 研究方向:Optics Web of Science 类别:Optics 文献信息 文献类型:Article 语种:English 入藏号: WOS:000369207700076 ISSN: 0030-4026 期刊信息 目录： Current Contents Connect® Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: DC4RJ Web of Science 核心合集中的 "引用的参考文献": 30 Web of Science 核心合集中的 "被引频次": 0 |
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