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
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页码:461 / 509
页数:49
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