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dc.contributor.authorWere, Festus A.
dc.contributor.authorOrwa, George Otieno
dc.contributor.authorOtieno, Romanus Odhiambo
dc.contributor.authorMakumi, Nicholas
dc.contributor.authorAldallal, Ramy
dc.date.accessioned2022-07-05T18:49:21Z
dc.date.available2022-07-05T18:49:21Z
dc.date.issued2022
dc.identifier.citationWere, F. A., Orwa, G. O., Otieno, R. O., Makumi, N., & Aldallal, R. (2022). Coverage Properties of a Neural Network Estimator of Finite Population Total in High-Dimensional Space. Journal of Mathematics, 2022, 1–7. https://doi.org/10.1155/2022/2431308en_US
dc.identifier.urihttps://doi.org/10.1155/2022/2431308
dc.identifier.urihttp://repository.must.ac.ke/handle/123456789/681
dc.description.abstractThe problem in nonparametric estimation of finite population total particularly when dealing with high-dimensional datasets is addressed in this paper. The coverage properties of a robust finite population total estimator based on a feedforward back propagation neural network developed with the help of a superpopulation model are computed, and a comparison with existing model-based estimators that can handle high-dimensional datasets is conducted to evaluate the estimator’s performance using simulated datasets. The results presented in this paper show good performance in terms of bias, MSE, and mean absolute error for the feedforward backpropagation neural network estimator as compared to other identified existing estimators of finite population total in high-dimensional datasets. In this regard, the paper recommends the use of the proposed estimator in estimating population parameters such as population total in the presence of high-dimensional datasets.en_US
dc.language.isoenen_US
dc.publisherJournal of Mathematicsen_US
dc.subjectNeural Network Estimatoren_US
dc.subjectNon-parametric Estimationen_US
dc.titleCoverage properties of a neural network estimator of finite population total in high-dimensional space.en_US
dc.typeArticleen_US


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