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Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data

Publication Type : Journal Article

Publisher : Computational Intelligence and Neuroscience

Source : Computational Intelligence and Neuroscience, 2021

Url : https://www.hindawi.com/journals/cin/2021/8593261/

Campus : Coimbatore

School : School of Physical Sciences

Department : Mathematics

Year : 2021

Abstract : In this manuscript, three new classes of log-type imputation techniques have been proposed to handle missing data when conducting surveys. The corresponding classes of point estimators have been derived for estimating the population mean. Their properties (Mean Square Errors and bias) have been studied. An extensive simulation study using data generated from normal, Poisson, and Gamma distributions, as well as real dataset, 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 : Pandey, A. K., Singh, G. N., Bhattacharyya, D., Ali, A.Q., Al-Thubaiti, S. Yakout, H. A. (2021) Some Classes of Logarithmic-Type Imputation Techniques for Handling Missing Data, Computational Intelligence and Neuroscience, DOI: https://doi.org/10.1155/2021/8593261

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