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An Exponential-Cum-Sine-Type Hybrid Imputation Technique for Missing Data

Publication Type : Journal Article

Publisher : Computational Intelligence and Neuroscience

Source : Computational Intelligence and Neuroscience, DOI: https://doi.org/10.1155/2021/4845569

Url : https://www.hindawi.com/journals/cin/2021/4845569/

Campus : Coimbatore

School : School of Physical Sciences

Department : Mathematics

Year : 2021

Abstract : In this study, a new exponential-cum-sine-type hybrid imputation technique has been proposed to handle missing data when conducting surveys. The properties of the corresponding point estimator for population mean have been examined in terms of bias and mean square errors. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions has been conducted to evaluate how the proposed estimator performs in comparison to several contemporary estimators. The results have been summarized, and discussion regarding real-life applications of the estimator follows.

Cite this Research Publication : Bhattacharyya, D., Singh, G. N., Jawa, T. M., Sayed-Ahmed, N., & Pandey, A. K. (2021). An Exponential-Cum-Sine-Type Hybrid Imputation Technique for Missing Data, Computational Intelligence and Neuroscience, DOI: https://doi.org/10.1155/2021/4845569

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