Posts / Sensitivity curve of the Harrell-Davis quantile estimator, Part 3


In the previous posts (1, 2), I have explored the sensitivity curves of the Harrell-Davis quantile estimator on the normal distribution, the exponential distribution, and the Cauchy distribution. In this post, I build these sensitivity curves for some additional distributions.

The classic Harrell-Davis quantile estimator (see harrell1982) is defined as follows:

$$ Q_{\operatorname{HD}}(p) = \sum_{i=1}^{n} W_{\operatorname{HD},i} \cdot x_{(i)},\quad W_{\operatorname{HD},i} = I_{i/n}(\alpha, \beta) - I_{(i-1)/n}(\alpha, \beta), $$

where $I_t(\alpha, \beta)$ is the regularized incomplete beta function, $\alpha = (n+1)p$, $\;\beta = (n+1)(1-p)$. In this post we consider the Harrell-Davis median estimator $Q_{\operatorname{HD}}(0.5)$.

The standardized sensitivity curve (SC) of an estimator $\hat{\theta}$ is given by

$$ \operatorname{SC}_n(x_0) = \frac{ \hat{\theta}_{n+1}(x_1, x_2, \ldots, x_n, x_0) - \hat{\theta}_n(x_1, x_2, \ldots, x_n) }{1 / (n + 1)} $$

Thus, the SC shows the standardized change of the estimator value for situation, when we add a new element $x_0$ to an existing sample $\mathbf{x} = \{ x_1, x_2, \ldots, x_n \}$. In the context of this post, we perform simulations using the different distributions given by their quantile functions that we denote as $F^{-1}$. Following the approach from [Maronna2019, Section 3.1], we define the sample $\mathbf{x}$ as

$$ \mathbf{x} = \Bigg\{ F^{-1}\Big(\frac{1}{n+1}\Big), F^{-1}\Big(\frac{2}{n+1}\Big), \ldots, F^{-1}\Big(\frac{n}{n+1}\Big) \Bigg\} $$

Now let’s explore the SC values for different sample sizes for $x_0 \in [-100; 100]$.

Distributions

We consider the following distributions:

DistributionSupportSkewnessTailness
Uniform(a=0, b=1)$[0;1]$SymmetricLight-tailed
Triangular(a=0, b=2, c=1)$[0;2]$SymmetricLight-tailed
Triangular(a=0, b=2, c=0.2)$[0;2]$Right-skewedLight-tailed
Beta(a=2, b=4)$[0;1]$Right-skewedLight-tailed
Beta(a=2, b=10)$[0;1]$Right-skewedLight-tailed
Normal(m=0, sd=1)$(-\infty;+\infty)$SymmetricLight-tailed
Weibull(scale=1, shape=2)$[0;+\infty)$Right-skewedLight-tailed
Student(df=3)$(-\infty;+\infty)$SymmetricLight-tailed
Gumbel(loc=0, scale=1)$(-\infty;+\infty)$Right-skewedLight-tailed
Exp(rate=1)$[0;+\infty)$Right-skewedLight-tailed
Cauchy(x0=0, gamma=1)$(-\infty;+\infty)$SymmetricHeavy-tailed
Pareto(loc=1, shape=0.5)$[1;+\infty)$Right-skewedHeavy-tailed
Pareto(loc=1, shape=2)$[1;+\infty)$Right-skewedHeavy-tailed
LogNormal(mlog=0, sdlog=1)$(0;+\infty)$Right-skewedHeavy-tailed
LogNormal(mlog=0, sdlog=2)$(0;+\infty)$Right-skewedHeavy-tailed
LogNormal(mlog=0, sdlog=3)$(0;+\infty)$Right-skewedHeavy-tailed
Weibull(shape=0.3)$[0;+\infty)$Right-skewedHeavy-tailed
Weibull(shape=0.5)$[0;+\infty)$Right-skewedHeavy-tailed
Frechet(shape=1)$(0;+\infty)$Right-skewedHeavy-tailed
Frechet(shape=3)$(0;+\infty)$Right-skewedHeavy-tailed

Sensitivity curves

Here are the results of numerical simulations:

Conclusion

As we can see, for $n \geq 15$ the actual impact of $x_0$ is negligible, which makes the Harrell-Davis median estimator a practically reasonable choice regardless of the distribution. However, we should be careful with heavy distributions like the Pareto or LogNormal distributions.

References


References (3)