Adaptive model selection method for a conditionally Gaussian semimartingale regression in continuous time
Abstract
This paper considers the problem of robust adaptive efficient estimating of a periodic function in a continuous time regression model with the dependent noises given by a general square integrable semimartingale with a conditionally Gaussian distribution. An example of such noise is the non-Gaussian Ornstein-Uhlenbeck-Levy processes. An adaptive model selection procedure, based on the improved weighted least square estimates, is proposed. Under some conditions on the noise distribution, sharp oracle inequality for the robust risk has been proved and the robust efficiency of the model selection procedure has been established. The numerical analysis results are given.
Cite
@article{arxiv.1811.05319,
title = {Adaptive model selection method for a conditionally Gaussian semimartingale regression in continuous time},
author = {Evgeny Pchelintsev and Serguei Pergamenshchikov},
journal= {arXiv preprint arXiv:1811.05319},
year = {2019}
}
Comments
50 pages, 2 figures. arXiv admin note: text overlap with arXiv:1710.03111, arXiv:1712.06454