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WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET

文献类型：期刊论文

作者Wu, Liang1,2; Ding, Yiming3,4
刊名ACTA MATHEMATICA SCIENTIA
出版日期2017
卷号37期号:1页码:205-222
关键词detection of long-range dependence self-similarity Hurst parameters wavelet analysis fractional Brownian sheet
英文摘要It is proposed a class of statistical estimators (H) over cap = ((H) over cap (1), . . . ,(H-d) over cap) for the Hurst parameters H = (H-1, . . . , H-d) of fractional Brownian field via multi-dimensional wavelet analysis and least squares, which are asymptotically normal. These estimators can be used to detect self -similarity and long-range dependence in multi-dimensional signals, which is important in texture classification and improvement of diffusion tensor imaging (DTI) of nuclear magnetic resonance (NMR). Some fractional Brownian sheets will be simulated and the simulated data are used to validate these estimators. We find that when H-i >= 1/2, the estimators. are accurate, and when H-i < 1/2, there are some bias.
WOS标题词Science & Technology ; Physical Sciences
类目[WOS]Mathematics
研究领域[WOS]Mathematics
关键词[WOS]TEXTURE ANALYSIS ; RANDOM-FIELDS ; MOTION ; COEFFICIENTS ; MODELS
收录类别SCI
语种英语
WOS记录号WOS:000393444200016
源URL[http://ir.wipm.ac.cn/handle/112942/10031]  
专题武汉物理与数学研究所_数学物理与应用研究部
作者单位1.Chinese Acad Sci, Wuhan Inst Phys & Math, Wuhan 430071, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Wuhan Inst Technol, Dept Math, Wuhan 430070, Peoples R China
4.Chinese Acad Sci, Wuhan Inst Phys & Math, Key Lab Magnet Resonance Biol Syst, Wuhan 430071, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Liang,Ding, Yiming. WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET[J]. ACTA MATHEMATICA SCIENTIA,2017,37(1):205-222.
APA Wu, Liang,&Ding, Yiming.(2017).WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET.ACTA MATHEMATICA SCIENTIA,37(1),205-222.
MLA Wu, Liang,et al."WAVELET-BASED ESTIMATOR FOR THE HURST PARAMETERS OF FRACTIONAL BROWNIAN SHEET".ACTA MATHEMATICA SCIENTIA 37.1(2017):205-222.

入库方式： OAI收割

来源：武汉物理与数学研究所

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