Novel predictive estimators using ranked set sampling. (7th December 2022)

• Record Type: Journal Article
• Authors: Bhushan, Shashi; Kumar, Anoop
• Is Part Of: Concurrency and computation. Volume 35:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-07
• Subjects: predictive estimation -- ranked set sampling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7435
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: This article suggests some novel logarithmic predictive estimators using ranked set sampling for the computation of population mean. The bias and mean square error of the proposed predictive estimators (PPEs) are determined up to the approximation of order one. The performance of the PPEs is examined regarding the contemporary existing predictive estimators (EPEs) utilizing simple random sampling (SRS) and ranked set sampling (RSS) and the efficiency conditions are obtained. Subsequently, a comprehensive computational examination is accomplished based on natural and artificially rendered symmetric and asymmetric populations to extend the theoretical results.