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

abstract

• 2018 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.

authors

• Chen, Willa

published proceedings

• STATISTICS & PROBABILITY LETTERS

author list (cited authors)

• Chen, W. W., & Deo, R. S.

citation count

• 0

complete list of authors

• Chen, Willa W||Deo, Rohit S

publication date

• January 2018

publisher

• Elsevier  Publisher

published in

• Statistics and Probability Letters  Journal

keywords

• Delete-d Jackknife Estimator
• Gini Index
• Self-normalization
• U Statistics

Digital Object Identifier (DOI)

• 10.1016/j.spl.2018.02.057

start page

• 95

end page

• 103

volume

• 138

URL

• http://dx.doi.org/10.1016/j.spl.2018.02.057