In the previous posts, I discussed the concept of a resistance function that shows the sensitivity of the given estimator to the low-density regions. I already showed how this function behaves for the mean, the sample median, and the Harrell-Davis median. In this post, I explore this function for the Hodges-Lehmann location estimator.

### The resistance function

As was shown in the previous post, we define the function of resistance to the low-density regions as follows:

\[R(T, n, s) = \max_{s \leq k \leq n} R(T, n, s, k), \]

\[R(T, n, s, k) = |T(\mathbf{x}_k) - T(\mathbf{x}_{k-s})|, \]

\[\mathbf{x}_k = \{ \underbrace{0, 0, \ldots, 0}_{k}, \underbrace{1, 1, \ldots, 1}_{n-k} \}, \]

where \(T\) is an estimator, \(n\) is the sample size, \(s\) is the number of sample values that jump from the first mode to the second one.

### Resistance of the Hodges-Lehmann location estimator

For a sample \(\mathbf{x} = \{ x_1, x_2, \ldots, x_n \}\), the Hodges-Lehmann location estimator is defined as follows:

\[\newcommand{\HL}{\operatorname{HL}} \HL(\mathbf{x}) = \underset{i < j}{\textrm{median}} \Bigg( \frac{x_i + x_j}{2} \Bigg). \]

Now it’s time to build the plot of \(R(T, n, s)\) that compares the mean, the sample median, and the Harrell-Davis median. In this experiment, we consider \(n \leq 100\), \(s \in \{1, 2, 3, 4, 5, 6\}\). Here are the plots:

As we can see, the resistance function value for the Hodges-Lehmann location estimator is \(0.5\) when the sample size \(n\) is sufficiently large.

### Deep view of the Hodges-Lehmann location estimator resistance function

Now we explore how \(R(\HL, n, s, k)\) depends on \(k\):

As we can see, most of the \(R(\HL, n, s, k)\) values are zeros expect two regions of \(k\) values in which the value is \(0.5\). These values correspond to the breakdown point of the Hodges-Lehmann location estimator (its asymptotic value is 29%). Thus, \(R(\HL, n, s, k) = 0.5\) for the \(k\) values around \(0.29 \cdot n\) and \(0.71 \cdot n\).

### Deep view of various resistance functions

In the previous section, we got interesting plots describing \(R(\HL, n, s, k)\). Now let us compare it with similar plots for other previously covered estimators (the mean, the sample median, and the Harrell-Davis median):

Compared to the sample median, the Hodges-Lehmann location estimator has two \(R=0.5\) regions instead of one, but it never reaches \(R=1\). Compared to the Harrell-Davis median, the Hodges-Lehmann location estimator has much higher \(R(\HL, n, s) = 0.5\), but \(R(\HL, n, s, k) = 0\) for the middle part of \(k\) values (which is much better than the positive values of the Harrell-Davis median). Considering the extremely high Gaussian efficiency of the Hodges-Lehmann location estimator (\(94\%\)) and its low breakdown point (\(29\%\)), this estimator can be a good choice for estimating the location of multimodal distributions.