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

• Authors: Irsa Sajjad , Muhammad Hanif , Nursel Koyuncu , Usman Shahzad , Nadia H. Al-Noor

Abstracts

EN

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

EN

percentage relative efficiency; percentage relative efficiency; percentage relative efficiency; percentage relative efficiency; percentage relative efficiency

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2021
• Volume: 22
• Issue: 1
• Pages: 207-216

Contributors

author

Irsa Sajjad

• Department of Lahore Business School - University of Lahore, Islamabad, Pakistan

• https://orcid.org/0000000272467043

author

Muhammad Hanif

• Department of Mathematics and Statistics - PMAS-Arid Agriculture University, Rawalpindi, Pakistan

• https://orcid.org/0000000219764452

author

Nursel Koyuncu

• Hacettepe University, Department of Statistics, Beytepe, Ankara, Turkey

• https://orcid.org/0000000310653411

author

Usman Shahzad

• Department of Mathematics and Statistics, International Islamic University, Islamabd, Pakistan
• Department of Mathematics and Statistics, PMAS-Arid Agriculture University, Rawalpindi, Pakistan

• https://orcid.org/0000000201785298

author

Nadia H. Al-Noor

• Department of Mathematics, College of Science, Mustansiriyah University, Baghdad, Iraq

• https://orcid.org/0000000244339044

Identifiers

DOI

10.21307/stattrans-2021-012

Biblioteka Nauki

1363609