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:

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


References (3)