Goodness-of-Fit Tests for Ornstein-Uhlenbeck Process
Statistics Theory
2013-05-16 v2 Statistics Theory
Abstract
We consider the goodness of fit testing problem for linear stochastic differential equation (Ornstein-Uhlenbeck process). The basic hypothesis is supposed to be composite with two-dimensional unknown parameter. We study two goodness of fit tests of Cramer-von Mises type based on empirical distribution function and on local time estimator of the invariant density. It is shown that the limit distributions of the underlying statistics under hypothesis do not depend on the unknown parameter.
Keywords
Cite
@article{arxiv.1203.3694,
title = {Goodness-of-Fit Tests for Ornstein-Uhlenbeck Process},
author = {Yury A. Kutoyants},
journal= {arXiv preprint arXiv:1203.3694},
year = {2013}
}
Comments
11 pages the paper has been wisdrawn by the author because this result is included in another paper of the same author