DefinePK

DefinePK hosts the largest index of Pakistani journals, research articles, news headlines, and videos. It also offers chapter-level book search.

Population Mean Estimation Using Ratio-cum Product Compromised-method of Imputation in Two-phase Sampling Scheme


Article Information

Title: Population Mean Estimation Using Ratio-cum Product Compromised-method of Imputation in Two-phase Sampling Scheme

Authors: Krishnajyothi Nath, B.K. Singh

Journal: Asian Journal of Mathematics & Statistics

HEC Recognition History
No recognition records found.

Publisher: Asian Network for Scientific Information (ANSInet)

Country: Pakistan

Year: 2019

Volume: 11

Issue: 1

Language: English

DOI: 10.10.3923/ajms.2018.27.39

Keywords: Mean Squared ErrorBiasSimple Random SamplingProductTwo-Phase samplingMissing dataRatiolarge sample approximation

Categories

Abstract

Background and Objective: In literature there has been a study on ratio cum product estimator of a finite population mean in two-phase sampling in sample surveys, but it lacks study when there is non-response on sample observations. So the main objective of this paper was to propose three generalized classes of ratio cum product compromised imputation techniques in presence of missing values in two-phase sampling design and its properties have been studied. Materials and Methods: The estimators were compared with other existing estimators in two different designs. The bias and M.S.E. of suggested estimators were derived in the form of population parameters using the concept of large sample approximations. Results: The results showed the superiority of the proposed estimators over the existing methods. Numerical studies are performed over two population data sets using the expressions of bias and M.S.E. and their efficiencies are compared with other existing estimators. Conclusion: It was observed that the proposed estimators were performing better than the estimators taken for comparisons in the presence of missing data.


Paper summary is not available for this article yet.

Loading PDF...

Loading Statistics...