Optimal designs for approximating the path of a stochastic process
✍ Scribed by Thomas Müller-Gronbach
- Publisher
- Elsevier Science
- Year
- 1996
- Tongue
- English
- Weight
- 592 KB
- Volume
- 49
- Category
- Article
- ISSN
- 0378-3758
No coin nor oath required. For personal study only.
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## Abstract This paper deals with optimal designs for Gaussian random fields with constant trend and exponential correlation structure, widely known as the Ornstein–Uhlenbeck process. Assuming the maximum likelihood approach, we study the optimal design problem for the estimation of the trend µ and
One can obtain a meaningful concept of informational entropy of deterministic functions as a direct consequence of Shannon information theory. When one applies this model to the trajectory generated by a stochastic process (for instance a process driven by a Langevin equation), one arrives at new co