2019/07/16 by Rose Baker, Baker, Rose
Engineering · Mathematics · #62g05 #Advanced Statistical Methods and Models #Applications (stat.AP) #Diverse Scientific and Engineering Research #FOS: Computer and information sciences #Statistical and numerical algorithms
paper · pdf · doi:10.48550/arxiv.1907.07015
openalex publication_date 2019/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The adaptive asymmetric trimmed mean is a known way of estimating central location, usually in conjunction with the bootstrap. It is here modified and applied to meta-analysis, as a way of dealing with outlying results by down-weighting the corresponding studies. This requires a modified bootstrap and a method of down-weighting studies, as opposed to removing single observations. This methodology is shown in analysis of some well-travelled datasets to down-weight outliers in agreement with other methods, and Monte-Carlo studies show that it does does not appreciably down-weight studies when outliers are absent. Conceptually simple, it does not make parametric assumptions about the outliers.