Non-asymptotic analysis and inference for an outlyingness induced winsorized mean

نویسندگان

چکیده

Robust estimation of a mean vector, topic regarded as obsolete in the traditional robust statistics community, has recently surged machine learning literature last decade. The latest focus is on sub-Gaussian performance and computability estimators non-asymptotic setting. Numerous are computationally intractable, which partly contributes to renewal interest estimation. centrality estimators, however, include trimmed sample median. latter best robustness but suffers low efficiency drawback. Trimmed median means, achieving have been proposed studied literature. This article investigates leading reveals that none them can resist greater than $$25\%$$ contamination data consequently introduces an outlyingness induced winsorized possible (can up $$50\%$$ without breakdown) meanwhile high efficiency. Furthermore, it for uncontaminated samples bounded error contaminated at given confidence level finite It be computed linear time.

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ژورنال

عنوان ژورنال: Statistical papers

سال: 2022

ISSN: ['2412-110X', '0250-9822']

DOI: https://doi.org/10.1007/s00362-022-01353-5