# Posts / Statistical efficiency of the Hodges-Lehmann median estimator, Part 2

In the previous post, we evaluated the relative statistical efficiency of the Hodges-Lehmann median estimator against the sample median under the normal distribution. In this post, we extended this experiment to a set of various light-tailed and heavy-tailed distributions.

## Introduction

The Hodges-Lehmann median estimator is defined as the sample median of all pair-wise averages of the given sample. However, there are various ways to define an explicit formula. Following an approach from park2020, we consider three options:

$$ \operatorname{HL}_1 = \underset{i < j}{\operatorname{median}}\Big(\frac{x_i + x_j}{2}\Big),\quad \operatorname{HL}_2 = \underset{i \leq j}{\operatorname{median}}\Big(\frac{x_i + x_j}{2}\Big),\quad \operatorname{HL}_3 = \underset{\forall i, j}{\operatorname{median}}\Big(\frac{x_i + x_j}{2}\Big). $$We also consider the classic Harrell-Davis quantile estimator which can also be used to estimate the median:

$$ Q_\textrm{HD}(p) = \sum_{i=1}^{n} W_{i} \cdot x_{(i)}, \quad W_{i} = I_{i/n}(a, b) - I_{(i-1)/n}(a, b), \quad a = p(n+1),\; b = (1-p)(n+1) $$where $I_t(a, b)$ denotes the regularized incomplete beta function, $x_{(i)}$ is the $i^\textrm{th}$ order statistics.

In addition, we consider the trimmed Harrell-Davis quantile estimator based on the highest density interval of size $1/\sqrt{n}$ (we denote it as $Q_{\operatorname{THD-SQRT}}$).

## Simulation study

In order to evaluate the relative statistical efficiency of the listed median estimators against the sample median, we use the following scheme:

- Enumerate different sample size values $n$ from $3$ to $20$.
- Enumerate various light-tailed and heavy-tailed distributions
- For each sample size, we generate $1\,000$ samples from the given distribution.
- For each sample, we estimate the median using the sample median, the Harrell-Davis quantile estimator $Q_{\operatorname{HD}}$, the trimmed Harrell-Davis quantile estimator $Q_{\operatorname{THD-SQRT}}$, and three versions of the Hodges-Lehmann median estimator $Q_{\operatorname{HL1}}$, $Q_{\operatorname{HL2}}$, $Q_{\operatorname{HL3}}$.
- Estimated the relative statistical efficiency of each case.

The results of the performed simulation study are shown in the following figure:

As we can see, the Hodges-Lehmann median estimator works great in the light-tailed case. However, in the heavy-tailed case, the Harrell-Davis quantile estimator and its trimmed modifications have better relative statistical efficiency.

## References

**[Harrell1982]**

Harrell, F.E. and Davis, C.E., 1982. A new distribution-free quantile estimator.*Biometrika*, 69(3), pp.635-640.

https://doi.org/10.2307/2335999**[Park2020]**

Park, Chanseok, Haewon Kim, and Min Wang. “Investigation of finite-sample properties of robust location and scale estimators.” Communications in Statistics-Simulation and Computation (2020): 1-27.

https://doi.org/10.1080/03610918.2019.1699114

### Backlinks (3)

- Gastwirth's location estimator (2022-06-07) 1
- Hodges-Lehmann-Sen shift and shift confidence interval estimators (2022-05-31)
- Statistical efficiency of the Hodges-Lehmann median estimator, Part 1 (2022-05-17) 2

### References (3)

- Statistical efficiency of the Hodges-Lehmann median estimator, Part 1 (2022-05-17) 2
- Publication announcement: 'Trimmed Harrell-Davis quantile estimator based on the highest densit... (2022-03-22) 5
- Investigation of finite-sample properties of robust location and scale estimators (2020) by Chanseok Park et al. 7