Modified ratio estimators using robust regression methods

ZAMAN T., Bulut H.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.48, no.8, pp.2039-2048, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 8
  • Publication Date: 2019
  • Doi Number: 10.1080/03610926.2018.1441419
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2039-2048
  • Keywords: Auxiliary information, Ratio-type Estimators, Relative Efficiency, Robust Regression Methods, Simple random sampling
  • Ondokuz Mayıs University Affiliated: Yes


When there is an outlier in the data set, the efficiency of traditional methods decreases. In order to solve this problem, Kadilar etal. (2007) adapted Huber-M method which is only one of robust regression methods to ratio-type estimators and decreased the effect of outlier problem. In this study, new ratio-type estimators are proposed by considering Tukey-M, Hampel M, Huber MM, LTS, LMS and LAD robust methods based on the Kadilar etal. (2007). Theoretically, we obtain the mean square error (MSE) for these estimators. We compared with MSE values of proposed estimators and MSE values of estimators based on Huber-M and OLS methods. As a result of these comparisons, we observed that our proposed estimators give more efficient results than both Huber M approach which was proposed by Kadilar etal. (2007) and OLS approach. Also, under all conditions, all of the other proposed estimators except Lad method are more efficient than robust estimators proposed by Kadilar etal. (2007). And, these theoretical results are supported with the aid of a numerical example and simulation by basing on data that includes an outlier.