Nonparametric model reconstruction for stochastic differential equations from discretely observed time-series data

• Ohkubo, Jun

Abstract

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.

Journal

• Physical Review E

  Physical Review E 84 (6), 2011-12

  American Physical Society (APS)

Details

• CRID: 1050564285679721984
• NII Article ID: 120003779166
• NII Book ID: AA11558033
• ISSN: 15393755
• ISSN: 24700053
• ISSN: 24700045
• HANDLE: 2433/152425
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • CiNii Articles