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

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  • Tolga Zaman
  • Hasan Bulut

Abstract

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.

Suggested Citation

  • Tolga Zaman & Hasan Bulut, 2020. "Modified regression estimators using robust regression methods and covariance matrices in stratified random sampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(14), pages 3407-3420, July.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:14:p:3407-3420
    DOI: 10.1080/03610926.2019.1588324
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