Random weighting-based quantile estimation via importance resampling

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.

Keywords:

• Importance resampling
• quantiles
• random weighting estimation
• sequential variance minimization

Additional information

Funding

The work of this paper was supported by the National Natural Science Foundation of China (Project No.41704016), the China Postdoctoral Science Foundation (Project No. 2017M613029) and the Postdoctoral Research Project Foundation of Shaanxi Province (Project No. 2017BSHEDZZ84).