# Middle non-zero quantile absolute deviation, Part 2

**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 one of the previous posts, I described the idea of the middle non-zero quantile absolute deviation. It’s defined as follows:

\[\operatorname{MNZQAD}(x, p) = \operatorname{QAD}(x, p, q_m), \]

\[q_m = \frac{q_0 + 1}{2}, \quad q_0 = \frac{\max(k - 1, 0)}{n - 1}, \quad k = \sum_{i=1}^n \mathbf{1}_{Q(x, p)}(x_i), \]

where \(\mathbf{1}\) is the indicator function

\[\mathbf{1}_U(u) = \begin{cases} 1 & \textrm{if}\quad u = U,\\ 0 & \textrm{if}\quad u \neq U, \end{cases} \]

and \(\operatorname{QAD}\) is the quantile absolute deviation

\[\operatorname{QAD}(x, p, q) = Q(|x - Q(x, p)|, q). \]

The \(\operatorname{MNZQAD}\) approach tries to work around a problem with tied values. While it works well in the generic case, there are some corner cases where the suggested metric behaves poorly. In this post, we discuss this problem and how to solve it.

### The problem

Let’s take 20 samples of size 1000 from the rectified Gaussian distribution and calculate \(\operatorname{MNZQAD}\) around the median for each of them. It could be done using the following R snippet:

```
qad <- function(x, p = 0.5, q = 0.5) as.numeric(quantile(abs(x - quantile(x, p)), q))
mnzqad <- function(x, p = 0.5) {
n <- length(x)
anchor <- quantile(x, p)
k <- sum(abs(x - anchor) < 1e-9)
q0 <- max(k - 1, 0) / (n - 1)
qm <- (q0 + 1) / 2
qad(x, 0.5, qm)
}
rrnorm <- function(n, sd = 1) {
x <- rnorm(n, sd = sd)
x[x < 0] <- 0
x
}
set.seed(1729)
replicate(20, mnzqad(rrnorm(1000)))
```

And here is the result:

```
0.6708304 0.0626490 0.6283213 0.6484299 0.0139355
0.6640861 0.0068413 0.0229421 0.5961456 0.6814358
0.6744908 0.6451489 0.6804007 0.0602365 0.7027132
0.6503397 0.0025354 0.0349211 0.0158567 0.0105813
```

As we can see, the results are not stable. Sometimes we have small values (like 0.007), and sometimes we have large values (like 0.703). The underlying problem is quite simple. If the number of zero values in the sample is larger than 500, the median is exactly zero, and the number of median-tied values \(k\) is around 500. It leads to evaluating the \(0.75^\textrm{th}\) quantile (\(q_m \approx 0.75\)). If the number of zero values in the sample is smaller than 500, the median is non-zero, and the number of median-tied values \(k\) is zero. It leads to evaluating the \(0.50^\textrm{th}\) quantile (\(q_m \approx 0.50\)).

### The solution

One of the ideas I have is about counting the total number of tied values in the sample instead of only median-tied values. It makes \(k\) much more stable in corner cases like the one above. Here is the updated R snippet:

```
mnzqad2 <- function(x, p = 0.5) {
n <- length(x)
anchor <- quantile(x, p)
k <- n - sum(table(x) == 1)
q0 <- max(k - 1, 0) / (n - 1)
qm <- (q0 + 1) / 2
qad(x, 0.5, qm)
}
set.seed(1729)
replicate(20, mnzqad2(rrnorm(1000)))
```

And here is the updated result:

```
0.6708304 0.6329019 0.6283213 0.6484299 0.7578973
0.6640861 0.6207138 0.6284881 0.5961456 0.6814358
0.6744908 0.6451489 0.6804007 0.6067494 0.7027132
0.6503397 0.6441379 0.6970224 0.6400928 0.6438555
```

As we can see, the new version of \(\operatorname{MNZQAD}\) is much more stable than the originally proposed version.