基于离散观测数据的随机微分方程非参数模型重构
Nonparametric model reconstruction for stochastic differential equation from discretely observed time-series data
摘要：
本文提出了一种由时间序列数据估计随机微分方程中与状态有关的漂移和扩散系数的方法。该格式不需要预先指定漂移和扩散系数的参数形式。为了执行非参数估计，将极大似然方法与基于核密度估计的概念相结合。为了处理离散观测或稀疏的时间序列数据，采用局部线性化方法，这使得快速估计。
英文摘要：
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
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。
