Preprint announcement: 'Weighted quantile estimators'

I have just published a preprint of a paper ‘Weighted quantile estimators’. It’s based on a series of my research notes that I have been writing since September 2020.

The paper preprint is available on arXiv: arXiv:2304.07265 [stat.ME]. The paper source code is available on GitHub: AndreyAkinshin/paper-wqe. You can cite it as follows:

• Andrey Akinshin (2023) “Weighted quantile estimators” arXiv:2304.07265

Abstract:

In this paper, we consider a generic scheme that allows building weighted versions of various quantile estimators, such as traditional quantile estimators based on linear interpolation of two order statistics, the Harrell-Davis quantile estimator and its trimmed modification. The obtained weighted quantile estimators are especially useful in the problem of estimating a distribution at the tail of a time series using quantile exponential smoothing. The presented approach can also be applied to other problems, such as quantile estimation of weighted mixture distributions.

Relevant blog posts

Here is the full list of the relevant blog posts:

• Weighted quantile estimators (2020-09-29)
• Quantile confidence intervals for weighted samples (2020-12-08)
• Using Kish's effective sample size with weighted quantiles (2021-07-06)
• Weighted trimmed Harrell-Davis quantile estimator (2022-04-19)
• Weighted quantile estimators for exponential smoothing and mixture distributions (2022-09-20)
• Weighted quantile estimation for a weighted mixture distribution (2022-10-25)
• Preprint announcement: 'Weighted quantile estimators' (2023-04-18)

BibTeX reference

@article{akinshin2023wqe,
  title = {Weighted quantile estimators},
  author = {Akinshin, Andrey},
  year = {2023},
  month = {4},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2304.07265},
  url = {https://arxiv.org/abs/2304.07265}
}