Higher order normalizing transformations of asymptotic U-statistics for removing bias, skewness and kurtosis

Yumi Fujioka; Yoshihiko Maesono

doi:10.1016/s0378-3758(99)00087-7

Higher order normalizing transformations of asymptotic U-statistics for removing bias, skewness and kurtosis

Yumi Fujioka, Yoshihiko Maesono

Department of Mathematical Sciences

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

This paper proposes a normalizing transformation which removes bias, skewness and kurtosis, simultaneously. Its convergence rate to the standard normal distribution is o(n^-1). The transformation is polynomial and monotone. We consider a class of asymptotic U-statistics, which includes most of interesting statistics.

Original language	English
Pages (from-to)	47-74
Number of pages	28
Journal	Journal of Statistical Planning and Inference
Volume	83
Issue number	1
DOIs	https://doi.org/10.1016/s0378-3758(99)00087-7
Publication status	Published - Jan 1 2000

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1016/s0378-3758(99)00087-7

Cite this

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abstract = "This paper proposes a normalizing transformation which removes bias, skewness and kurtosis, simultaneously. Its convergence rate to the standard normal distribution is o(n-1). The transformation is polynomial and monotone. We consider a class of asymptotic U-statistics, which includes most of interesting statistics.",

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AB - This paper proposes a normalizing transformation which removes bias, skewness and kurtosis, simultaneously. Its convergence rate to the standard normal distribution is o(n-1). The transformation is polynomial and monotone. We consider a class of asymptotic U-statistics, which includes most of interesting statistics.

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Higher order normalizing transformations of asymptotic U-statistics for removing bias, skewness and kurtosis

Abstract

All Science Journal Classification (ASJC) codes

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