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

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Statistics in Transition New Series

Polish Statistical Association

Central Statistical Office of Poland

Subject: Economics, Statistics & Probability

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ISSN: 1234-7655
eISSN: 2450-0291

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VOLUME 22 , ISSUE 1 (March 2021) > List of articles

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

Irsa Sajjad / Muhammad Hanif / Nursel Koyuncu / Usman Shahzad * / Nadia H. Al-Noor

Keywords : supplementary attributes, ratio-type estimators, SRS, robust regression tools, percentage relative efficiency

Citation Information : Statistics in Transition New Series. Volume 22, Issue 1, Pages 207-216, DOI: https://doi.org/10.21307/stattrans-2021-012

License : (CC BY-NC-ND 4.0)

Received Date : 05-May-2020 / Accepted: 27-November-2020 / Published Online: 03-March-2021

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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.

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