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Subsampling based inference for U statistics under thick tails using self-normalization

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  • Chen, Willa W.
  • Deo, Rohit S.

Abstract

We consider a subsampling approach for U-statistics based on a self-normalized statistic recently suggested by Shao (2015) and establish its consistency. A simple finite averaging of the self-normalizer is proposed to reduce the average length of the associated confidence interval. Our procedure is robust to thick tailed distributions and skewed distributions and in simulations for the Gini index, is seen to provide an improvement over the t-statistic based intervals.

Suggested Citation

  • Chen, Willa W. & Deo, Rohit S., 2018. "Subsampling based inference for U statistics under thick tails using self-normalization," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 95-103.
    • Handle: RePEc:eee:stapro:v:138:y:2018:i:c:p:95-103
    DOI: 10.1016/j.spl.2018.02.057
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    References listed on IDEAS

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