On the optimal designs for the prediction of complex Ornstein-Uhlenbeck processes

TL;DR

This paper investigates optimal sampling designs for predicting complex Ornstein-Uhlenbeck processes, deriving explicit criteria and showing that entropy-based designs are equidistant, with numerical examples illustrating these findings.

Contribution

It provides explicit formulas for optimal design criteria and compares entropy-based and IMSPE-based sampling strategies for complex OU processes.

Findings

Entropy-based optimal designs are equidistant.

IMSPE-based optimal designs may differ from equidistant arrangements.

Numerical experiments illustrate the theoretical results.

Abstract

Physics, chemistry, biology or finance are just some examples out of the many fields where complex Ornstein-Uhlenbeck (OU) processes have various applications in statistical modelling. They play role e.g. in the description of the motion of a charged test particle in a constant magnetic field or in the study of rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov used such a process to model the so-called Chandler wobble, the small deviation in the Earth's axis of rotation. A common problem in these applications is deciding how to choose a set of a sample locations in order to predict a random process in an optimal way. We study the optimal design problem for the prediction of a complex OU process on a compact interval with respect to integrated mean square prediction error (IMSPE) and entropy criteria. We derive the exact forms of both criteria, moreover, we show that optimal designs based on entropy criterion are equidistant, whereas the IMSPE based ones may differ from it. Finally, we present some numerical experiments to illustrate selected cases of optimal designs for small number of sampling locations.