| PS {tea} | R Documentation |
Sample Path Stability Algorithm
Description
An Implementation of the heuristic algorithm for choosing the optimal sample fraction proposed in Caeiro & Gomes (2016), among others.
Usage
PS(data, j = 1)
Arguments
data |
vector of sample data |
j |
digits to round to. Should be |
Details
The algorithm searches for a stable region of the sample path, i.e. the plot of a tail index estimator with respect to k. This is done in two steps. First the estimation of the tail index for every k is rounded to j digits and the longest set of equal consecutive values is chosen. For this set the estimates are rounded to j+2 digits and the mode of this subset is determined. The corresponding biggest k-value, denoted k0 here, is the optimal number of data in the tail.
Value
k0 |
optimal number of upper order statistics, i.e. number of exceedances or data in the tail |
threshold |
the corresponding threshold |
tail.index |
the corresponding tail index |
References
Caeiro, J. and Gomes, M.I. (2016). Threshold selection in extreme value analysis. Extreme Value Modeling and Risk Analysis:Methids and Applications, 69–86.
Gomes, M.I. and Henriques-Rodrigues, L. and Fraga Alves, M.I. and Manjunath, B. (2013). Adaptive PORT-MVRB estimation: an empirical comparison of two heuristic algorithms. Journal of Statistical Computation and Simulation, 83, 1129–1144.
Gomes, M.I. and Henriques-Rodrigues, L. and Miranda, M.C. (2011). Reduced-bias location-invariant extreme value index estimation: a simulation study. Communications in Statistic-Simulation and Computation, 40, 424–447.
Examples
data(danish)
PS(danish)