# Preprint announcement: 'Finite-sample bias-correction factors for the median absolute deviation based on the Harrell-Davis quantile estimator and its trimmed modification'

I have just published a preprint of a paper ‘Finite-sample bias-correction factors for the median absolute deviation based on the Harrell-Davis quantile estimator and its trimmed modification’. It’s based on a series of my research notes that I have been writing since February 2021.

The paper preprint is available on arXiv: arXiv:2207.12005 [stat.ME]. The paper source code is available on GitHub: AndreyAkinshin/paper-mad-factors. You can cite it as follows:

• Andrey Akinshin (2022) “Finite-sample bias-correction factors for the median absolute deviation based on the Harrell-Davis quantile estimator and its trimmed modification,” arXiv:2207.12005

Abstract:

The median absolute deviation is a widely used robust measure of statistical dispersion. Using a scale constant, we can use it as an asymptotically consistent estimator for the standard deviation under normality. For finite samples, the scale constant should be corrected in order to obtain an unbiased estimator. The bias-correction factor depends on the sample size and the median estimator. When we use the traditional sample median, the factor values are well known, but this approach does not provide optimal statistical efficiency. In this paper, we present the bias-correction factors for the median absolute deviation based on the Harrell-Davis quantile estimator and its trimmed modification which allow us to achieve better statistical efficiency of the standard deviation estimations. The obtained estimators are especially useful for samples with a small number of elements.

### Relevant blog posts

Here is the full list of relevant blog posts:

### BibTeX reference

@article{akinshin2022madfactors,
title = {Finite-sample bias-correction factors for the median absolute deviation based on the Harrell-Davis quantile estimator and its trimmed modification},
author = {Akinshin, Andrey},
year = {2022},
month = {7},
publisher = {arXiv},
doi = {10.48550/ARXIV.2207.12005},
url = {https://arxiv.org/abs/2207.12005}
}


The source code of this post and all the relevant files are available on GitHub.
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