# Quantile absolute deviation of the Uniform distribution

Update: this blog post is a part of research that aimed to build a new measure of statistical dispersion called quantile absolute deviation. A preprint with final results is available on arXiv: arXiv:2208.13459 [stat.ME]. Some information in this blog post can be obsolete: please, use the preprint as the primary reference.

In this post, we derive the exact equation for the quantile absolute deviation around the median of the Uniform distribution.

## Preparation

We consider the quantile absolute deviation around the median defined as follows:


where $$\Q$$ is a quantile estimator.

We are looking for the asymptotic value of $$\QAD(X, p)$$. For simplification, we denote it by $$v_p$$:

$v_p = \lim_{n \to \infty} \E[\Q(|X-M|, p)],$

where $$M$$ is the true median of the distribution.

By the definition of quantiles, this can be rewritten as:

$\PR(|X_1 - M| < v_p) = p,$

which is the same as

$\PR(-v_p < X_1 - M < v_p) = p.$

Hence,

$\PR(M - v_p < X_1 < M + v_p) = p.$

If $$F$$ is the CDF of the considered distribution, the above equality can be rewritten as

$F(M + v_p) - F(M - v_p) = p. \tag{1}$

## Uniform distribution

We consider the standard uniform distribution $$\mathcal{U}(0, 1)$$ given by the CDF $$F(x)=x$$ on $$[0;1]$$ with the median value $$M=0.5$$. From(1), we have:

$(0.5 + v_p) - (0.5 - v_p) = p,$

which is the same as

$v_p = p / 2.$

Thus, if $$X \sim \mathcal{U}(0, 1)$$,

$\lim_{n \to \infty} \E[\QAD(X, p)] = \frac{p}{2}.$

Here is the corresponding plot: