Publication announcement: 'Trimmed Harrell-Davis quantile estimator based on the highest density interval of the given width'



Since the beginning of previous year, I have been working on building a quantile estimator that provides an optimal trade-off between statistical efficiency and robustness. At the end of the year, I published the corresponding preprint where I presented a description of such an estimator: arXiv:2111.11776 [stat.ME]. The paper source code is available on GitHub: AndreyAkinshin/paper-thdqe.

Finally, the paper was published in Communications in Statistics - Simulation and Computation. 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

Here is the corresponding BibTeX reference:

@article{akinshin2022thdqe,
  author = {Andrey Akinshin},
  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},
  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.}
}

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