Goodness-of-Fit Tests for Ornstein-Uhlenbeck Process
arXiv:1203.3694
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.
11 pages the paper has been wisdrawn by the author because this result is included in another paper of the same author