Standard trimmed Harrell-Davis median estimator

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 suggested a new measure of dispersion called the standard quantile absolute deviation around the median (\(\operatorname{SQAD}\)) which can be used as an alternative to the median absolute deviation (\(\operatorname{MAD}\)) as a consistent estimator for the standard deviation under normality. The Gaussian efficiency of \(\operatorname{SQAD}\) is \(54\%\) (comparing to \(37\%\) for MAD), and its breakdown point is \(32\%\) (comparing to \(50\%\) for MAD). \(\operatorname{SQAD}\) is a symmetric dispersion measure around the median: the interval \([\operatorname{Median} - \operatorname{SQAD}; \operatorname{Median} + \operatorname{SQAD}]\) covers \(68\%\) of the distribution. In the case of the normal distribution, this corresponds to the interval \([\mu - \sigma; \mu + \sigma]\).

If we use \(\operatorname{SQAD}\), we accept the breakdown point of \(32\%\). This makes the sample median a non-optimal choice for the median estimator. Indeed, the sample median has high robustness (the breakdown point is \(50\%\)), but relatively poor Gaussian efficiency. If we use \(\operatorname{SQAD}\), it doesn’t make sense to require a breakdown point of more than \(32\%\). Therefore, we could trade the median robustness for efficiency and come up with a complementary measure of the median for \(\operatorname{SQAD}\).

In this post, we introduce the standard trimmed Harrell-Davis median estimator which shares the breakdown point with \(\operatorname{SQAD}\) and provides better finite-sample efficiency comparing to the sample median.

Trimmed Harrell-Davis quantile estimator

The concept of this estimator is fully covered in my recent paper [Akinshin2022]. Here I just briefly recall the basic idea.

Let \(x\) be a sample with \(n\) elements: \(x = \{ x_1, x_2, \ldots, x_n \}\). We assume that all sample elements are sorted (\(x_1 \leq x_2 \leq \ldots \leq x_n\)) so that we could treat the \(i^\textrm{th}\) element \(x_i\) as the \(i^\textrm{th}\) order statistic \(x_{(i)}\). Based on the given sample, we want to build an estimation of the \(p^\textrm{th}\) quantile \(Q(p)\).

The classic Harrell-Davis quantile estimator (see [Harrell1982]) suggests the following approach:

\[Q_{\operatorname{HD}}(p) = \sum_{i=1}^{n} W_{\operatorname{HD},i} \cdot x_i,\quad W_{\operatorname{HD},i} = I_{i/n}(\alpha, \beta) - I_{(i-1)/n}(\alpha, \beta), \]

where \(I_x(\alpha, \beta)\) is the regularized incomplete beta function, \(\alpha = (n+1)p\), \(\;\beta = (n+1)(1-p)\).

When we switch to the trimmed modification of this estimator, we perform summation only within the highest density interval \([L;R]\) of \(\operatorname{Beta}(\alpha, \beta)\) of size \(D\) (as a rule of thumb, we can use \(D = 1 / \sqrt{n}\)):

\[Q_{\operatorname{THD},D}(p) = \sum_{i=1}^{n} W_{\operatorname{THD},D,i} \cdot x_i, \quad W_{\operatorname{THD},D,i} = F_{\operatorname{THD},D}(i / n) - F_{\operatorname{THD},D}((i - 1) / n), \]

\[F_{\operatorname{THD},D}(x) = \begin{cases} 0 & \textrm{for }\, x < L,\\ \big( I_x(\alpha, \beta) - I_L(\alpha, \beta) \big) / \big( I_R(\alpha, \beta) \big) - I_L(\alpha, \beta) \big) \big) & \textrm{for }\, L \leq x \leq R,\\ 1 & \textrm{for }\, R < x. \end{cases} \]

Thus, we use only sample elements with the highest weight coefficients (\(W_{\operatorname{THD},D,i}\)) and ignore sample elements with small weight coefficients. It allows us to get a high statistical efficiency (which is close to the efficiency of the classic Harrell-Davis quantile estimator) and a good robustness level (in most cases, outliers have zero impact on the final result).

Standard trimmed Harrell-Davis median estimator

The highest density interval size \(D\) defines the portion of the sample that is actually used to estimate quantiles using \(Q_{\operatorname{THD},D}\). Therefore, the asymptotic breakdown point of \(Q_{\operatorname{THD},D}\) is \(1-D\). In order to make \(Q_{\operatorname{THD},D}\) consistent with \(\operatorname{SQAD}\) in terms of robustness, we should use \(D=\Phi(1)-\Phi(-1) \approx 0.6827\). We call such an estimator the standard trimmed Harrell-Davis median estimator and denote by \(Q_{\operatorname{STHD}}\).

In the scope of this research, we are interested only in the median estimator \(Q_{\operatorname{STHD}}(0.5)\). For the median estimator, the beta function \(\operatorname{Beta}(\alpha, \beta)\) becomes symmetric around \(0.5\) since \(\alpha = \beta = (n + 1) / 2\); the interval \([L;R]\) becomes \([\Phi(-1); \Phi(1)]\).

\[Q_{\operatorname{STHD}}(0.5) = \sum_{i=1}^{n} W_{\operatorname{STHD},i} \cdot x_i, \quad W_{\operatorname{STHD},i} = F_{\operatorname{STHD}}(i / n) - F_{\operatorname{STHD}}((i - 1) / n), \]

\[F_{\operatorname{STHD}}(x) = \begin{cases} 0 & \textrm{for }\, x < \Phi(-1),\\ \dfrac{ I_x(\frac{n+1}{2}, \frac{n+1}{2}) - I_L(\frac{n+1}{2}, \frac{n+1}{2}) }{I_R(\frac{n+1}{2}, \frac{n+1}{2}) - I_L(\frac{n+1}{2}, \frac{n+1}{2})} & \textrm{for }\, \Phi(-1) \leq x \leq \Phi(1),\\ 1 & \textrm{for }\, \Phi(1) < x. \end{cases} \]

Finite-sample efficiency of standard trimmed Harrell-Davis median estimator

In order to evaluate the actual finite-sample efficiency of \(Q_{\operatorname{STHD}}(0.5)\), we perform a simple Monte-Carlo simulation. We enumerate various sample sizes; for each sample size we generate multiple random samples from the standard normal distribution; estimate the mean, the sample median, and \(Q_{\operatorname{STHD}}(0.5)\); evaluate the relative efficiency of the sample median and \(Q_{\operatorname{STHD}}(0.5)\) against the mean as the ratio of the corresponding variance values.

Here are the results for \(n \leq 100\):

As we can see, for small samples, \(Q_{\operatorname{STHD}}(0.5)\) is noticeably more efficient than the sample median.

And here are the results for \(n \leq 100\,000\):

As we can see, asymptotically both estimators converge to the same value which is \(2 / \pi \approx 63.66%\). It makes sense: the Harrell-Davis median estimator is asymptotically consistent with the traditional sample median (see [Yoshizawa1985]). Since \(Q_{\operatorname{STHD}}(0.5)\) is “between” these two estimators, it is expected that all of them converge to the same value.


  • [Akinshin2022]
    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
  • [Yoshizawa1985]
    Carl N Yoshizawa, Pranab K Sen, and C Edward Davis. “Asymptotic equivalence of the Harrell- Davis median estimator and the sample median”. In: Communications in Statistics-Theory and Methods 14.9 (1985), pp. 2129–2136.
  • [Harrell1982]
    Harrell, F.E. and Davis, C.E., 1982. A new distribution-free quantile estimator. Biometrika, 69(3), pp.635-640.

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