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On the identification of the pointwise Holder exponent of the generalized multifractional Brownian motion

机译:广义多重分数布朗运动的逐点Holder指数的辨识

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The generalized multifractional Brownian motion (GMBM) is a continuous Gaussian process that extends the classical fractional Brownian motion (FBM) and multifractional Brownian motion (MBM) (SIAM Rev. 10 (1968) 422; INRIA Res. Rept. 2645 (1995); Rev. Mat. lberoamericana 13 (1997) 19; Fractals: Theory and Applications in Engineering, Springer, Berlin, 1999, pp. 17-32; Statist. Inference Stochastic Process. 3 (2000) 7). As is the case for the MBM, the Holder regularity of the GMBM varies from point to point. However, and this is the main interest of the GMBM, contrary to the MBM, these variations may be very erratic: As shown in (J. Fourier Anal. Appl. 8 (2002) 581), the pointwise Holder function {alpha(X) (t)}(t) of the GMBM may be any lim inf of continuous functions with values in a compact of (0, 1). This feature makes the GMBM a good candidate to model complex data such as textured images or multifractal processes. For the GMBM to be useful in applications, it is necessary that its Holder exponents may be estimated from discrete data. This work deals with the problem of identifying the pointwise Holder function H of the GMBM: While it does not seem easy to do so when H is an arbitrary lim inf of continuous functions, we obtain below the following a priori unexpected result: As soon as the pointwise holder function of GMBM belongs to the first class of Baire (i.e. when {alpha(X)(t )}(t) is a limit of continuous functions) it may be estimated almost surely at any point t. We also derive a Central Limit Theorem for our estimator. Thus, even very irregular variations of the Holder regularity of the GMBM may be detected and estimated in practice. This has important consequences in applications of the GMBM to signal and image processing. It may also lead to new methods for the practical computation of multifractal spectra. We illustrate our results on both simulated and real data. (C) 2003 Elsevier B.V. All rights reserved.
机译:广义分数布朗运动(GMBM)是连续的高斯过程,其扩展了古典分数布朗运动(FBM)和分数布朗运动(MBM)(SIAM Rev. 10(1968)422; INRIA Res。Rept。2645(1995); Rev. Mat。lberoamericana 13(1997)19; Fractals:Theory and Applications in Engineering,Springer,Berlin,1999,pp.17-32; Statist。Inference Stochastic Process。3(2000)7)。就像MBM一样,GMBM的持有人规律也随点而异。但是,这是GMBM的主要目的,与MBM相反,这些变化可能非常不稳定:如(J. Fourier Anal。Appl。8(2002)581)中所示,逐点Holder函数{alpha(X GMBM的(t)}(t)可以是连续函数的任何极限,其值的紧凑值为(0,1)。此功能使GMBM成为对复杂数据(例如纹理图像或多重分形过程)建模的理想选择。为了使GMBM在应用中有用,必须从离散数据中估计其Holder指数。这项工作解决了识别GMBM的逐点Holder函数H的问题:当H是连续函数的任意限制时,看起来似乎并不容易,但我们在下面获得了以下先验的意外结果: GMBM的逐点持有者函数属于第一类Baire(即,当{alpha(X)(t)}(t)是连续函数的极限)时,几乎可以肯定地在任何点t上进行估计。我们还为估计量导出了一个中心极限定理。因此,在实践中甚至可以检测和估计GMBM的持有人规律的非常不规则的变化。这对于GMBM在信号和图像处理中的应用具有重要的影响。这也可能导致新的方法来实际计算多重分形光谱。我们用模拟和真实数据说明了我们的结果。 (C)2003 Elsevier B.V.保留所有权利。

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