LESS VULNERABLE CONFIDENCE AND SIGNIFICANCE PROCEDURES FOR LOCATION BASED ON A SINGLE SAMPLE : TRIMMING/WINSORIZATION 1

By J. Tukey, D. Mclaughlin. 2016

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A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, methodology): “…deviations from the means of each distribution (60.7% → 55.5%; 91.9% → 55.5% for MM1 effect; 275% → 168% for MM2 effect; and 217% → 182% and 275% → 182% for MM-Min effect), akin to a Winsorization procedure [Tukey and McLaughlin 1963], which is a widely used technique for outlier treatment.”
Mean Estimation and Regression Under Heavy-Tailed Distributions: A Survey (2019, unknown): “This idea is one of the most classical tools in robust statistics and we refer to Tukey and McLaughlin [78], Huber and Ronchetti [39], Bickel [8], Stigler [74] for early work on the theoretical properties of the trimmed-mean estimator.”
Robust multivariate mean estimation: The optimality of trimmed mean (2019, methodology): “This idea is one of the most classical tools in robust statistics and we refer to Tukey and McLaughlin [28], Huber and Ronchetti [15], Bickel [1], Stigler [26] for early work on the theoretical properties of the trimmed-mean estimator.”