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

By Tolga Zaman, Hasan Bulut. Communications in Statistics - Theory and Methods, 2020

64 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/14e6d9caf9d59b50e3fe91f699fba7f689868279

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In Statistics and Probability

1. Hasan Bulut
2. Tolga Zaman

Citations in the corpus

• An efficient family of robust-type estimators for the population variance in simple and stratified random sampling (2021, methodology): “We use the data in Zaman and Bulut (2019) to compare the performances between the robust ratio-type of population variance and classical estimators based on MSE equations.”