A new family of robust regression estimators utilizing robust regression tools and supplementary attributes

Sajjad Irsa (), Hanif Muhammad (), Shahzad Usman (), Koyuncu Nursel () and Al-Noor Nadia H. ()
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Sajjad Irsa: Department of Lahore Business School, University of Lahore, Islamabad, Pakistan
Shahzad Usman: Department of Mathematics and Statistics, PMAS-Arid Agriculture University, Rawalpindi, Pakistan
Koyuncu Nursel: Hacettepe University, Department of Statistics, Beytepe, Ankara, Turkey
Al-Noor Nadia H.: Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, Iraq

Statistics in Transition New Series, 2021, vol. 22, issue 1, 207-216

Abstract: Zaman and Bulut (2018a) developed a class of estimators for a population mean utilising LMS robust regression and supplementary attributes. In this paper, a family of estimators is proposed, based on the adaptation of the estimators presented by Zaman (2019), followed by the introduction of a new family of regression-type estimators utilising robust regression tools (LAD, H-M, LMS, H-MM, Hampel-M, Tukey-M, LTS) and supplementary attributes. The mean square error expressions of the adapted and proposed families are determined through a general formula. The study demonstrates that the adapted class of the Zaman (2019) estimators is in every case more proficient than that of Zaman and Bulut (2018a). In addition, the proposed robust regression estimators based on robust regression tools and supplementary attributes are more efficient than those of Zaman and Bulut (2018a) and Zaman (2019).The theoretical findings are supported by real-life examples.

Keywords: supplementary attributes; ratio-type estimators; SRS; robust regression tools; percentage relative efficiency.. (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:22:y:2021:i:1:p:207-216:n:8

DOI: 10.21307/stattrans-2021-012

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