Posts / Gastwirth's location estimator


Let $x = \{ x_1, x_2, \ldots, x_n \}$ be a random sample. The Gastwirth’s location estimator is defined as follows:

$$ 0.3 \cdot Q_{⅓}(x) + 0.4 \cdot Q_{½}(x) + 0.3 \cdot Q_{⅔}(x), $$

where $Q_p$ is an estimation of the $p^{\textrm{th}}$ quantile (using classic sample quantiles).

This estimator could be quite interesting from a practical point of view. On the one hand, it’s robust (the breakdown point ⅓) and it has better statistical efficiency than the classic sample median. On the other hand, it has better computational efficiency than other robust and statistical efficient measures of location like the Harrell-Davis median estimator or the Hodges-Lehmann median estimator.

In this post, we conduct a short simulation study that shows its behavior for the standard Normal distribution and the Cauchy distribution.

Simulation study

Let’s conduct the following simulation:

Here are the results:

Based on this plot, we can do the following observations:

Of course, a more sophisticated study is required for reliable conclusions, but this simulation shows that Gastwirth’s location estimator is quite promising. It provides an interesting trade-off between statistical efficiency, computational efficiency, and robustness. It could be useful if we want to improve the statistical efficiency of the sample median for the light-tailed cases, keeping a decent breakdown point (⅓) and computational efficiency.

References


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