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Article | 03-March-2021

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

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

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

Statistics in Transition New Series, Volume 22 , ISSUE 1, 207–216

Research paper | 31-October-2017

ROBUST REGRESSION IN MONTHLY BUSINESS SURVEY

alternative techniques of estimation, less sensitive to outliers, have been proposed in the statistical literature. In this paper we attempt to apply and assess some robust regression methods (LTS, M estimation, S-estimation, MM-estimation) in the business survey conducted within the framework of official statistics.

Grażyna Dehnel

Statistics in Transition New Series, Volume 16 , ISSUE 1, 137–152

Article | 07-July-2017

M-ESTIMATORS IN BUSINESS STATISTICS

Recent years have seen a dynamic development in statistical methods for analysing data contaminated with outliers. One of the more important techniques that can deal with outlying observations is robust regression, which represents four decades of research. Until recently the implementation of robust regression methods, such as M-estimation or MM-estimation, was limited owing to their iterative nature. With advances in computing power and the growing availability of statistical packages, such

Grażyna Dehnel

Statistics in Transition New Series, Volume 17 , ISSUE 4, 749–762

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