Optimized estimation for population mean using conventional and non-conventional measures under the joint influence of measurement error and non-response. Issue 12 (13th August 2018)

• Record Type: Journal Article
• Authors: Irfan, Muhammad; Javed, Maria; Lin, Zhengyan
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 12(2018)
• Page Start: 2385
• Page End: 2403
• Publication Date: 2018-08-13
• Subjects: Auxiliary variable -- bias -- mean square error -- measurement error -- non-response; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1464571
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Most of the research work in the theory of survey sampling only deals with the sampling errors under the assumptions: (i) there is a complete response and (ii) recorded information from individuals is correct but in practice it is not always true. Non-sampling errors like non-response and measurement errors (MEs) mostly creep into the survey and become more influential for estimators than sampling errors. Considering this practical situation of non-response and MEs jointly, we proposed an optimum class of estimators for population mean under simple random sampling using conventional and non-conventional measures. Bias and mean square error of the proposed estimators are derived up to first degree of approximation. Moreover, a simulation study is conducted to assess the performance of new estimators which proves that proposed estimators are more efficient than the traditional Hansen and Hurwitz estimator and other competing estimators.