On statistical estimation and inferences in optional regression models
Statistics Theory
2021-03-16 v1 Statistical Finance
Statistics Theory
Abstract
The main object of investigation in this paper is a very general regression model in optional setting - when an observed process is an optional semimartingale depending on an unknown parameter. It is well-known that statistical data may present an information flow/filtration without usual conditions. The estimation problem is achieved by means of structural least squares (LS) estimates and their sequential versions. The main results of the paper are devoted to the strong consistency of such LS-estimates. For sequential LS-estimates the property of fixed accuracy is proved.
Cite
@article{arxiv.2103.08148,
title = {On statistical estimation and inferences in optional regression models},
author = {Mohamed Abdelghani and Alexander Melnikov and Andrey Pak},
journal= {arXiv preprint arXiv:2103.08148},
year = {2021}
}
Comments
to be published in Statistics: A Journal of Theoretical and Applied Statistics