Most Effective Sampling Scheme for Prediction of Stationary Stochastic Processes

Autor: Mohammad Mehdi Saber, Zohreh Shishebor, M. M. Abd El Raouf, E.H. Hafez, Ramy Aldallal
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Complexity, Vol 2022 (2022)
Druh dokumentu: article
ISSN: 1099-0526
DOI: 10.1155/2022/4997675
Popis: The problem of finding optimal sampling schemes has been resolved in two models. The novelty of this study lies in its cost efficiency, specifically, for the applied problems with expensive sampling process. In discussed models, we show that some observations counteract other ones in prediction mechanism. The autocovariance function of underlying process causes mentioned result. Our interesting result is that, although removing neutralizing observations convert sampling scheme to nonredundant case, it causes to worse prediction. A simulation study confirms this matter, too.
Databáze: Directory of Open Access Journals