Optimum Predictor in Stationary First-order Moving Average Process

Saber, Mohammad Mehdi; Khorshidian, K.

doi:10.1007/s40995-021-01172-7

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2022

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“…is is left as future work. Also, in Saber and Khorshidian [18], the best predictor in stationary first-order moving average model has been represented. So, another interesting study may be searching and exploring neutralizing and neutralized samples in moving average models.…”

Section: Discussionmentioning

confidence: 99%

Most Effective Sampling Scheme for Prediction of Stationary Stochastic Processes

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%