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

Author

Listed:
  • Sajjad Irsa

    (Department of Lahore Business School, University of Lahore, Islamabad, Pakistan)

  • Hanif Muhammad
  • 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)

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.

Suggested Citation

  • Sajjad Irsa & Hanif Muhammad & Shahzad Usman & Koyuncu Nursel & Al-Noor Nadia H., 2021. "A new family of robust regression estimators utilizing robust regression tools and supplementary attributes," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 207-216, March.
    • Handle: RePEc:vrs:stintr:v:22:y:2021:i:1:p:207-216:n:8
    DOI: 10.21307/stattrans-2021-012
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