Weak convergence for random weighting estimation of smoothed quantile processes

This paper presents a new random weighting method for smoothed quantile processes. A theory is established for random weighting estimation of smoothed quantile processes. It proves the weak convergence of the random weighting estimation error. Experiments and comparison analysis demonstrate that the proposed random weighting method can effectively estimate statistics, and the achieved accuracy and convergence speed are much higher than those of the Bootstrap method.