Robust estimations from distribution structures: Mean.

As one of the most fundamental problems in statistics, robust location estimation has many prominent solutions, such as the symmetric trimmed mean, symmetric Winsorized mean, Hodges–Lehmann estimator, Huber M-estimator, and median of means. Recent studies suggest that their biases concerning the mean can be quite different in asymmetric distributions, but the underlying mechanisms largely remain unclear. This study exploited a semiparametric method to classify distributions by the asymptotic orderliness of location estimates with varying breakdown points, showing their interrelations and connections to parametric distributions. Further deductions explain why the Winsorized mean typically has smaller biases compared to the trimmed mean; two sequences of semiparametric robust mean estimators emerge. Building on the $\gamma$-$U$-orderliness, the superiority of the median Hodges–Lehmann mean is discussed.

These works have been publically deposited in this Github since one year ago for a PNAS paper (I hidden some previous versions after updated new versions, https://github.com/tubanlee/SRM16). I am introducing this work in YouTube and Quora, if you are interested, please visit: https://www.youtube.com/@Iobiomathematics or https://www.quora.com/profile/Tuobang-Li-1/answers . Also, the manuscript has been deposited in Zenodo Tuobang Li. (2023). Robust estimations for semiparametric models: Mean. https://doi.org/10.5281/zenodo.6629988 or https://www.researchgate.net/publication/377973944_Robust_estimations_from_distribution_structures_I_Mean

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