A computational framework for estimation of mean in presence of observational error. (27th January 2022)

• Record Type: Journal Article
• Authors: Vishwakarma, Gajendra K.; Singh, Neha; Kumar, Neelesh
• Is Part Of: Concurrency and computation. Volume 34:Number 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-27
• Subjects: auxiliary variable -- observational error -- optimum sample size -- two‐phase sampling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6842
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: This article presents a computational framework for estimation of the mean in two‐phase sampling mechanism using multi‐auxiliary variables when observational errors are observed in sample data. A competent class of estimators is proposed which encompasses several existing estimation techniques under observational error. The properties of the proposed class of estimators have been explored under large sample approximation using the Taylor series expansions method. A strive is done to obtain the optimum sample sizes for a certain cost of the survey. The impact of observational errors is assessed over the mean square error of the estimators. The simulation study is demonstrated to reflect the dominant nature of the proposed class of estimators over the existing one.