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arXiv 1111.1916math.STstat.TH
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多尺度扩散的半参数漂移和扩散估计

Semi-Parametric Drift and Diffusion Estimation for Multiscale Diffusions

Sebastian Krumscheid, Grigorios A. Pavliotis, Serafim Kalliadasis

摘要:

本文研究了具有(至少)两个分离的特征时间尺度的多尺度扩散过程的有效动力学的统计推断问题。更准确地说,我们试图确定参数的有效方程描述的动态扩散的较长的时间尺度,即在一个均匀化的框架。我们研究的情况下,漂移和扩散系数在有效的动态空间依赖性和依赖于多个未知参数。众所周知,经典的估计,如最大似然估计和二次变异的路径估计,无法获得合理的估计参数的有效动态时,基于观测的基础上的多尺度扩散。我们提出了一种新的算法估计漂移和扩散系数的有效动力学的基础上的半参数框架。我们证明了广泛的数值模拟的一些选定的例子,该算法表现良好,当应用到多尺度扩散的数据。这些例子也说明了该算法可以有效地用于获得准确和无偏估计。

英文摘要:

We consider the problem of statistical inference for the effective dynamics of multiscale diffusion processes with (at least) two widely separated characteristic time scales. More precisely, we seek to determine parameters in the effective equation describing the dynamics on the longer diffusive time scale, i.e. in a homogenization framework. We examine the case where both the drift and the diffusion coefficients in the effective dynamics are space-dependent and depend on multiple unknown parameters. It is known that classical estimators, such as Maximum Likelihood and Quadratic Variation of the Path Estimators, fail to obtain reasonable estimates for parameters in the effective dynamics when based on observations of the underlying multiscale diffusion. We propose a novel algorithm for estimating both the drift and diffusion coefficients in the effective dynamics based on a semi-parametric framework. We demonstrate by means of extensive numerical simulations of a number of selected examples that the algorithm performs well when applied to data from a multiscale diffusion. These examples also illustrate that the algorithm can be used effectively to obtain accurate and unbiased estimates.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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