Exponential-Ratio-Type Imputation Class of Estimators using Nonconventional Robust Measures of Dispersions
Ahmed Audu, Mojeed Abiodun Yunusa, Aminu Bello Zoramawa, Samaila Buda, Ran Vijay Kumar Singh
Asian Journal of Probability and Statistics · pp. 59–74 · Published 28 Oct 2021
10.9734/ajpas/2021/v15i230351Abstract
Human-assisted surveys, such as medical and social science surveys, are frequently plagued by non-response or missing observations. Several authors have devised different imputation algorithms to account for missing observations during analyses. Nonetheless, several of these imputation schemes' estimators are based on known auxiliary variable parameters that can be influenced by outliers. In this paper, we suggested new classes of exponential-ratio-type imputation method that uses parameters that are robust against outliers. Using the Taylor series expansion technique, the MSE of the class of estimators presented was derived up to first order approximation. Conditions were also specified for which the new estimators were more efficient than the other estimators studied in the study. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.
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Cited by 6
A. Audu, S. A. Abdulazeez, A. Danbaba, Y. M. Ahijjo, A. Gidado, M. A. Yunusa · Asian Research Journal of Mathematics · 2022
Ajibola Yahya Yusuf, Ahmed Audu, Mojeed Abiodun Yunusa · FUDMA JOURNAL OF SCIENCES · 2024
Showing 2 of 6 known citations — external sources report more than can currently be individually listed.
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