Browsing by Author "Aljohani, H"

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    Mean estimation using an efficient class of estimators based on simple random sampling: Simulation and applications
    (2024-02) Kumar, A; Siddiqui, A; Mustafa, M; Hussam, E; Aljohani, H; Almulhim, F
    In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean 𝑌̄ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators.
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    Novel imputation methods under stratified simple random sampling
    (2024-04) Kumar, A; Bhushan, S; Mustafa, M; Aldallal, R; Aljohani, H; Almulhim, F
    This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called ‘novel’. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.