Modified regression estimators using robust regression methods and covariance matrices in stratified random sampling

ZAMAN T., Bulut H.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, vol.49, no.14, pp.3407-3420, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 14
  • Publication Date: 2020
  • Doi Number: 10.1080/03610926.2019.1588324
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3407-3420
  • Keywords: Regression-type estimators, robust regression methods, robust covariance matrices, auxiliary information, relative efficiency, stratified random sampling
  • Ondokuz Mayıs University Affiliated: Yes


This article proposes new regression-type estimators by considering Tukey-M, Hampel M, Huber MM, LTS, LMS and LAD robust methods and MCD and MVE robust covariance matrices in stratified sampling. Theoretically, we obtain the mean square error (MSE) for these estimators. We compare the efficiencies based on MSE equations, between the proposed estimators and the traditional combined and separate regression estimators. As a result of these comparisons, we observed that our proposed estimators give more efficient results than traditional approaches. And, these theoretical results are supported with the aid of numerical examples and simulation based on data sets that include outliers.