A distribution-free m-out-of-n bootstrap approach to testing symmetry about an unknown median

Vyacheslav Lyubchich, Xingyu Wang, Andrew Heyes and Yulia R. Gel

Computational Statistics & Data Analysis, 2016, vol. 104, issue C, 1-9

Abstract: Testing for symmetry about an unknown median is a ubiquitous problem in mathematical statistics, particularly, for nonparametric rank-based methods, and in a broad range of applied studies, from economics and business to biology, ecology, and medicine. However, the challenge still remains on how to derive a symmetry test with a good power performance and at the same time delivering a reliable Type I Error estimate. To overcome this problem, a new data-driven m-out-of-n bootstrap method is introduced for testing symmetry about an unknown median. The asymptotic properties of the developed m-out-of-n bootstrap tests are investigated along with their empirical finite-sample performance. The new tests are illustrated by applications to legal studies and wildlife monitoring.

Keywords: Testing for symmetry; Bootstrap; Resampling; Environmental monitoring; Mercury bioaccumulation; Skewness (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:104:y:2016:i:c:p:1-9

DOI: 10.1016/j.csda.2016.05.004

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