Comparison of Two Estimation Methods of Missing Values Using Pitman-Closeness Criterion

Hamaz, Abdelghani; Ibazizen, Mohamed

doi:10.1080/03610920802521190

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2022

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Cited by 9 publications

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“…For prediction in lattice data or discrete time series, we need to apply a model to data. Two predictors for a single missing value in a stationary autoregressive process of order one (AR(1)) model have been compared by Hamaz and Ibazizen [12] and Saadatmand et al [13]. ey considered the following model:…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%