Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
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