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

Autor: Pandey AK; Department of Mathematics, School of Physical Sciences, DIT University, Dehradun, Uttarakhand 248 009, India., Singh GN; Department of Mathematics & Computing, Indian Institute of Technology (ISM), Dhanbad 826 004, Jharkhand, India., Bhattacharyya D; Department of Mathematics & Computing, Indian Institute of Technology (ISM), Dhanbad 826 004, Jharkhand, India., Ali AQ; Mharat Academy for Training & Development, Ibb, Yemen., Al-Thubaiti S; Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11 099, Taif 21 944, Saudi Arabia., Yakout HA; Department of Physics, College of Science, King Khalid University, PO Box 9004, Abha 61 413, Saudi Arabia.
Jazyk: angličtina
Zdroj: Computational intelligence and neuroscience [Comput Intell Neurosci] 2021 Dec 20; Vol. 2021, pp. 8593261. Date of Electronic Publication: 2021 Dec 20 (Print Publication: 2021).
DOI: 10.1155/2021/8593261
Abstrakt: 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.
Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper.
(Copyright © 2021 Awadhesh K. Pandey et al.)
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje