Random Weighting Estimation of Confidence Intervals for Quantiles

Authors


Author to whom correspondence should be addressed.

Summary

This paper presents a new random weighting method for confidence interval estimation for the sample inline image-quantile. A theory is established to extend ordinary random weighting estimation from a non-smoothed function to a smoothed function, such as a kernel function. Based on this theory, a confidence interval is derived using the concept of backward critical points. The resultant confidence interval has the same length as that derived by ordinary random weighting estimation, but is distribution-free, and thus it is much more suitable for practical applications. Simulation results demonstrate that the proposed random weighting method has higher accuracy than the Bootstrap method for confidence interval estimation.

Ancillary