Journal
FOUNDATIONS OF COMPUTATIONAL MATHEMATICS
Volume 23, Issue 1, Pages 33-84Publisher
SPRINGER
DOI: 10.1007/s10208-021-09541-9
Keywords
Parameter inference; Diffusion process; Data-driven homogenization; Filtering; Bayesian inference; Langevin equation
Ask authors/readers for more resources
We investigate the problem of drift estimation for two-scale continuous time series. By using filtered data and maximum likelihood estimators, we avoid the traditional subsampling method and demonstrate the asymptotic unbiasedness of the proposed estimators. Furthermore, by combining the filtered data methodology with Bayesian techniques, we provide a complete uncertainty quantification of the inference procedure.
We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coefficient of the homogenized equation requires pre-processing of the data, often in the form of subsampling; this is because the two-scale equation and the homogenized single-scale equation are incompatible at small scales, generating mutually singular measures on the path space. We avoid subsampling and work instead with filtered data, found by application of an appropriate kernel function, and compute maximum likelihood estimators based on the filtered process. We show that the estimators we propose are asymptotically unbiased and demonstrate numerically the advantages of our method with respect to subsampling. Finally, we show how our filtered data methodology can be combined with Bayesian techniques and provide a full uncertainty quantification of the inference procedure.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available