Ultimate efficiency of designs for processes of Ornstein-Uhlenbeck type
Statistics Theory
2013-07-11 v2 Statistics Theory
Abstract
For a process governed by a linear Ito stochastic differential equation of the form dX(t)=[a(t)+b(t)X(t)]dt + \sigma(t)dW(t) we prove an existence of optimal sampling designs with strictly increasing sampling times. We derive an asymptotic Fisher information matrix, which we take as a reference in assessing a quality of finite-point sampling designs. The results are extended to a broader class of Ito stochastic differential equations satisfying a certain condition. We give an example based on the Gompertz growth law refuting a generally accepted opinion that small-sample designs lead to a very high level of efficiency.
Keywords
Cite
@article{arxiv.1306.6222,
title = {Ultimate efficiency of designs for processes of Ornstein-Uhlenbeck type},
author = {V. Lacko},
journal= {arXiv preprint arXiv:1306.6222},
year = {2013}
}