A New Family of Robust Regression Estimators Utilizing Robust Regression Tools and Supplementary Attributes

Sajjad, Irsa; Hanify, Muhammad; Koyuncu, Nursel; Shahzad, Usman; Al-Noor, Nadia H.

doi:10.21307/stattrans-2021-012

Author

Sajjad Irsa (University of Lahore, Islamabad, Pakistan), Hanify Muhammad (PMAS-Arid Agriculture University, Rawalpindi, Pakistan), Koyuncu Nursel (Hacettepe University, Department of Statistics, Beytepe, Ankara, Turkey), Shahzad Usman (International Islamic University, Islamabd, Pakistan), Al-Noor Nadia H. (College of Science, Mustansiriyah University, Baghdad, Iraq)

Title

A New Family of Robust Regression Estimators Utilizing Robust Regression Tools and Supplementary Attributes

Source

Statistics in Transition, 2021, vol. 22, nr 1, s. 207-216, rys., tab., aneks, bibliogr. s. 214-215

Keyword

Odporne metody statystyczne, Estymatory, Analiza regresji
Robust statistical methods, Estimators, Regression analysis

Note

summ.

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. (original abstract)

Accessibility

The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice

Full text

Bibliography

ABD-ELFATTA,H A. M., EL-SHERPIENY, E. A., MOHAMED, S. M., ABDOU, O. F., (2010). Improvement in estimating the population mean in simple random sampling using information on auxiliary attribute, Appl. Math. Comput., Vol. 215, pp. 4198-4202.
KOYUNCU, N., (2012). Efficient estimators of population mean using auxiliary attributes. Applied Mathematics and Computation, Vol. 218, pp. 10900-10905.
NAIK, V. D., GUPTA, P. C., (1996). A note on estimation of mean with known population of an auxiliary character, J. Indian Soc. Agr. Stat., Vol. 48, pp. 151-158.
NASIR, A., AHMAD, I., HANIF, M., SHAHZAD, U., (2018). Robust-regression-type estimators for improving mean estimation of sensitive variables by using auxiliary information, Commun. Stat. Theory Methods, doi:10.1080/03610926.2019.1645857.
SHAHZAD, U., (2016). On the Estimation Of Population Mean Under Systematic Sampling Using Auxiliary Attributes, Oriental Journal Physical Sciences, Vol. 1 (1&2), pp. 17-22.
SHAHZAD, U., HANIF, M., KOYUNCU, N., SANAULLAH, A., (2018). On the estimation of population variance using auxiliary attribute in absence and presence of non-response, Electronic Journal of Applied Statistical Analysis, Vol. 11, pp. 608-621.
SUKHATME, P. V., SUKHATME, B. V., (1970). Sampling Theory of Surveys with Applications, Iowa State University Press, Ames, USA,.
ZAMAN, T., BULUT, H., (2018a). Modified ratio estimators for population mean using robust regression based on auxiliary attribute, IJMR, Vol. 4, pp. 1-6.
ZAMAN, T., BULUT, H., (2018b). Modified ratio estimators using robust regression methods, Commun. Stat. Theory Methods, doi:10.1080/03610926.2018.1441419.
ZAMAN, T., (2019). Improvement of modified ratio estimators using robust regression methods. Applied Mathematics and Computation, Vol. 348, pp. 627-631.

Cited by

ISSN

1234-7655

Language

eng

URI / DOI

BazEkon - The Main Library of the Cracow University of Economics

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