Robust ratio-type estimators for finite population mean in simple random sampling: A simulation study

ZAMAN T., Bulut H., Yadav S. K.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, vol.34, no.25, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 25
  • Publication Date: 2022
  • Doi Number: 10.1002/cpe.7273
  • Journal Name: CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: efficiency, outliers, ratio-type estimators, robust regression techniques, REGRESSION
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In this study, ratio estimators are proposed by utilizing some robust techniques to get the maximum benefit of the auxiliary variable for the estimation of the population mean in simple random sampling. The expressions for mean squared error are derived for the first degree of approximation. Theoretical comparisons demonstrate that the suggested estimators having robust regression estimates perform better than the existing estimators under certain conditions. Theoretical findings are supported with the aid of the original dataset in an application. In addition, a simulation study is also conducted to evaluate the performance of the suggested estimators.