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Random weighting-based quantile estimation via importance resampling

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Listed:
  • Wenhui Wei
  • Shesheng Gao
  • Bingbing Gao
  • Yongmin Zhong
  • Chengfan Gu
  • Zhaohui Gao

Abstract

This paper presents a new method to estimate the quantiles of generic statistics by combining the concept of random weighting with importance resampling. This method converts the problem of quantile estimation to a dual problem of tail probabilities estimation. Random weighting theories are established to calculate the optimal resampling weights for estimation of tail probabilities via sequential variance minimization. Subsequently, the quantile estimation is constructed by using the obtained optimal resampling weights. Experimental results on real and simulated data sets demonstrate that the proposed random weighting method can effectively estimate the quantiles of generic statistics.

Suggested Citation

  • Wenhui Wei & Shesheng Gao & Bingbing Gao & Yongmin Zhong & Chengfan Gu & Zhaohui Gao, 2019. "Random weighting-based quantile estimation via importance resampling," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(19), pages 4820-4833, October.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:19:p:4820-4833
    DOI: 10.1080/03610926.2018.1496256
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