📝 SummarXiv

Abstract

In this paper we refine the procedure proposed by Lin et al. (2015) to estimate the density at a given quantile based on a resampling method. The approach consists on generating multiple samples of the zero-mean Gaussian variable from which a least square estimator is constructed. The main advantage of the proposed method is that it provides an estimation directly at the quantile of interest, thus achieving the parametric rate of convergence. In this study, we investigate the critical role of the variance of the sampled Gaussians on the accuracy of the estimation. We provide theoretical guarantees on this variance that ensure the consistency of the estimator, and we propose a gridsearch algorithm for automatic variance selection in practical applications. We demonstrate the performance of the proposed estimator in simulations and compare the results with those obtained using kernel density estimator.