A computational strategy for estimation of mean using optimal imputation in presence of missing observation

Subhash Kumar Yadav; Gajendra K Vishwakarma; Dinesh K Sharma

doi:10.1038/s41598-024-57264-y

A computational strategy for estimation of mean using optimal imputation in presence of missing observation

Sci Rep. 2024 Mar 18;14(1):6433. doi: 10.1038/s41598-024-57264-y.

Authors

Subhash Kumar Yadav¹, Gajendra K Vishwakarma², Dinesh K Sharma³

Affiliations

¹ Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India.
² Department of Mathematics and Computing, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, 826004, India. vishwagk@rediffmail.com.
³ Department of Business, Management and Accounting, University of Maryland Eastern Shore, Princess Anne, MD, 21853, USA.

Abstract

In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.

Keywords: Estimation; Imputation; Missing data.