 # Statistical efficiency of the Hodges-Lehmann median estimator, Part 1

In this post, we evaluate the relative statistical efficiency of the Hodges-Lehmann median estimator against the sample median under the normal distribution. We also compare it with the efficiency of the Harrell-Davis quantile estimator.

### 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.

### 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 $$30$$.
• For each sample size, we generate $$10\,000$$ samples from the normal distribution.
• For each sample, we estimate the median using the sample median, the Harrell-Davis quantile estimator, and three versions of the Hodges-Lehmann median estimator.
• Since all considered estimators are unbiased under the normal distribution, the relative statistical efficiency is just a ratio between the variance of the sample median and the variance of the target median estimator.

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

As we can see, for $$n\geq 6$$, all three versions of the Hodges-Lehmann median estimator outperform the Harrell-Davis quantile estimator in terms of relative statistical efficiency under the normal distribution.

In the next post, we perform more simulations study to get a better understanding of the properties of the Hodges-Lehmann median estimator.

• [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