Journal of Multivariate Analysis
A robust estimator for the tail index of Pareto-type distributions
Computational Statistics & Data Analysis
An adjusted boxplot for skewed distributions
Computational Statistics & Data Analysis
Detecting influential observations in Kernel PCA
Computational Statistics & Data Analysis
Tail index estimation in the presence of long-memory dynamics
Computational Statistics & Data Analysis
An asymptotically unbiased minimum density power divergence estimator for the Pareto-tail index
Journal of Multivariate Analysis
Hi-index | 0.03 |
Pareto-type distributions are extreme value distributions for which the extreme value index @c0. Classical estimators for @c0, like the Hill estimator, tend to overestimate this parameter in the presence of outliers. The empirical influence function plot, which displays the influence that each data point has on the Hill estimator, is introduced. To avoid a masking effect, the empirical influence function is based on a new robust GLM estimator for @c. This robust GLM estimator is used to determine high quantiles of the data generating distribution, allowing to flag data points as unusually large if they exceed this high quantile.