Tempered fractional Brownian motion: Wavelet estimation, modeling and testing

被引:7
|
作者
Boniece, B. Cooper [1 ]
Didier, Gustavo [2 ]
Sabzikar, Farzad [3 ]
机构
[1] Washington Univ, Dept Math & Stat, 1 Brookings Dr, St Louis, MO 63105 USA
[2] Tulane Univ, Math Dept, 6823 St Charles Ave, New Orleans, LA 70118 USA
[3] Iowa State Univ, Dept Stat, 2438 Osborn Dr, Ames, IA 50011 USA
关键词
Fractional Brownian motion; Semi-long range dependence; Tempered fractional Brownian motion; Turbulence; Wavelets; LONG-RANGE DEPENDENCE; PARAMETER-ESTIMATION; STOCHASTIC-PROCESSES; MEMORY PARAMETER; REGRESSION; TRANSFORM; DIFFUSION; THEOREM;
D O I
10.1016/j.acha.2019.11.004
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The Davenport spectrum is a modification of the classical Kolmogorov spectrum for the inertial range of turbulence that accounts for non-scaling low frequency behavior. Like the classical fractional Brownian motion vis-a-vis the Kolmogorov spectrum, tempered fractional Brownian motion (tfBm) is a new model that displays the Davenport spectrum. The autocorrelation of the increments of tfBm displays semilong range dependence (hyperbolic and quasi-exponential decays over moderate and large scales, respectively), a phenomenon that has been observed in a wide range of applications from wind speeds to geophysics to finance. In this paper, we use wavelets to construct the first estimation method for tfBm and a simple and computationally efficient test for fBm vs tfBm alternatives. The properties of the wavelet estimator and test are mathematically and computationally established. An application of the methodology shows that tfBm is a better model than fBm for a geophysical flow data set. (c) 2019 Elsevier Inc. All rights reserved.
引用
收藏
页码:461 / 509
页数:49
相关论文
共 50 条
  • [41] Bayesian Sequential Estimation of a Drift of Fractional Brownian Motion
    Cetin, U.
    Novikov, A.
    Shiryaev, A. N.
    SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS, 2013, 32 (03): : 288 - 296
  • [42] Simulation of Fractional Brownian Motion and Estimation of Hurst Parameter
    Pashko, Anatolii
    Sinyayska, Olga
    Oleshko, Tetiana
    15TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET - 2020), 2020, : 632 - 637
  • [43] Bayesian estimation of the Hurst parameter of fractional Brownian motion
    Chen, Chen-Yueh
    Shafie, Khalil
    Lin, Yen-Kuang
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2017, 46 (06) : 4760 - 4766
  • [44] Estimation for translation of a process driven by fractional Brownian motion
    Rao, BLSP
    STOCHASTIC ANALYSIS AND APPLICATIONS, 2005, 23 (06) : 1199 - 1212
  • [45] On drift parameter estimation in models with fractional Brownian motion
    Kozachenko, Y.
    Melnikov, A.
    Mishura, Y.
    STATISTICS, 2015, 49 (01) : 35 - 62
  • [46] TEMPERED FRACTIONAL MULTISTABLE MOTION AND TEMPERED MULTIFRACTIONAL STABLE MOTION
    Fan, Xiequan
    Vehel, Jacques Levy
    ESAIM-PROBABILITY AND STATISTICS, 2019, 23 : 37 - 67
  • [47] A note on the use of fractional Brownian motion for financial modeling
    Rostek, S.
    Schoebel, R.
    ECONOMIC MODELLING, 2013, 30 : 30 - 35
  • [48] Piecewise fractional Brownian motion for modeling sea clutter
    Liu Ning-Bo
    Guan Jian
    Huang Yong
    Wang Guo-Qing
    He You
    ACTA PHYSICA SINICA, 2012, 61 (19)
  • [49] Asymptotic Properties of Parameter Estimators in Vasicek Model Driven by Tempered Fractional Brownian Motion
    Mishura, Yuliya
    Ralchenko, Kostiantyn
    Dehtiar, Olena
    AUSTRIAN JOURNAL OF STATISTICS, 2025, 54 (01) : 61 - 81
  • [50] Evaluation for convergence of wavelet-based estimators on fractional Brownian motion
    Kawasaki, S
    Morita, H
    2000 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, PROCEEDINGS, 2000, : 470 - 470