Threshold Estimation Approaches
Alternative Hill Plot
Averaged Hill Plot
Minimizing the AMSE of the Hill estimator with respect to k
A Double Bootstrap Procedure for Choosing the Optimal Sample Fraction
A Bias-based procedure for Choosing the Optimal Sample Fraction
Automated Approach for Interpreting the Hill-Plot
Gerstengarbe Plot
A Bias-based procedure for Choosing the Optimal Threshold
A Double Bootstrap Procedure for Choosing the Optimal Sample Fraction
The Generalized Pareto Distribution (GPD)
Parameter estimation for the Generalized Pareto Distribution (GPD)
A Single Bootstrap Procedure for Choosing the Optimal Sample Fraction
A Single Bootstrap Procedure for Choosing the Optimal Sample Fraction
Minimizing the AMSE of the Hill estimator with respect to k
Minimizing the distance between the empirical tail and a theoretical P...
Sample Path Stability Algorithm
QQ-Estimator-Plot
QQ-Plot against the generalized Pareto distribution for given number o...
Adaptive choice of the optimal sample fraction in tail index estimatio...
Sum Plot
Threshold Estimation Approaches
Sequential Goodness of Fit Testing for the Generalized Pareto Distribu...
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.