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• W4387609908 abstract "AbstractAn extensive amount of research is emphasized on survey designs and estimation procedures related to rare and clustered characteristics of a population. Adaptive Sampling design is the most applicable probabilistic technique to estimate the mean or total of the variable of interest, bearing rarity and clustered characteristics. Since rarity is regarded as a time-dependent feature, such surveys need to be organized constantly over time. No studies so far have investigated the effect of time in the estimation context of Adaptive Sampling. This research therefore captures the need to synthesize this periodic information when conducting a survey using Adaptive Sampling design. A recursive process is employed here that improves the estimate of the population parameter from a practical perspective. “Kalman Filtering” is a well known recursive procedure to use past data. Later, statisticians were able to use that Kalman Filtering technique with the Bayesian formulation. This Bayesian approach is proposed to employ here to improve the estimation in the context of Adaptive Sampling design, utilizing the past data. A simulation study is carried out and it is concluded that the suggested approach substantially improves the estimation accuracy.Keywords: Adaptive SamplingGeneralized regression estimatorHorvitz–Thompson estimatorKalman filteringSimulationMATHEMATICS SUBJECT CLASSIFICATION: 62D05 AcknowledgementsThe authors gratefully acknowledge the support received from the referees which enabled them to produce this improved version out of the original submission.Disclosure statementNo potential conflict of interest was reported by the author(s)." @default.
• W4387609908 created "2023-10-14" @default.
• W4387609908 creator A5022748630 @default.
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• W4387609908 date "2023-10-13" @default.
• W4387609908 modified "2023-10-14" @default.
• W4387609908 title "Application of Kalman Filtering with Bayesian formulation in adaptive sampling" @default.
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• W4387609908 doi "https://doi.org/10.1080/03610918.2023.2265084" @default.
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