The repository contains auxiliary data for the paper "Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width":
- The source code of the paper:
thdqe.Rmd,preamble.tex,before_body.tex,references.bib,*.R - Calculated data for Simulation 2:
efficiency.csv - The reference implementation of the suggested estimator:
thdqe.R
The paper was published by Taylor & Francis in Communications in Statistics — Simulation and Computation on 17 March 2022, available online: https://www.tandfonline.com/10.1080/03610918.2022.2050396
You can cite it as follows:
Andrey Akinshin (2022) Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width, Communications in Statistics - Simulation and Computation, DOI: 10.1080/03610918.2022.2050396
BiBTeX reference:
@article{akinshin2022thdqe,
author = {Akinshin, Andrey},
title = {Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width},
journal = {Communications in Statistics - Simulation and Computation},
pages = {1-11},
year = {2022},
month = {3},
publisher = {Taylor & Francis},
doi = {10.1080/03610918.2022.2050396},
URL = {https://www.tandfonline.com/doi/abs/10.1080/03610918.2022.2050396},
eprint = {https://www.tandfonline.com/doi/pdf/10.1080/03610918.2022.2050396},
abstract = {Traditional quantile estimators that are based on one or two order statistics are a common way to estimate distribution quantiles based on the given samples. These estimators are robust, but their statistical efficiency is not always good enough. A more efficient alternative is the Harrell-Davis quantile estimator which uses a weighted sum of all order statistics. Whereas this approach provides more accurate estimations for the light-tailed distributions, it’s not robust. To be able to customize the tradeoff between statistical efficiency and robustness, we could consider a trimmed modification of the Harrell-Davis quantile estimator. In this approach, we discard order statistics with low weights according to the highest density interval of the beta distribution.}
}arXiv preprint: arXiv:2111.11776 [stat.ME]