Parametric estimation for a parabolic linear SPDE model based on sampled data
Statistics Theory
2019-10-01 v1 Statistics Theory
Abstract
We consider parametric estimation for a parabolic linear second order stochastic partial differential equation (SPDE) from high frequency data which are observed in time and space. By using thinned data obtained from the high frequency data, adaptive estimators of the coefficient parameters including the volatility parameter of a parabolic linear SPDE model are proposed. Moreover, we give some examples and simulation results of the adaptive estimators of the SPDE model based on the high frequency data.
Cite
@article{arxiv.1909.13557,
title = {Parametric estimation for a parabolic linear SPDE model based on sampled data},
author = {Yusuke Kaino and Masayuki Uchida},
journal= {arXiv preprint arXiv:1909.13557},
year = {2019}
}
Comments
38 pages, 73 figures