Abstract
This paper investigates a class of location invariant non-positive moment-type estimators of extreme value index, which is highly flexible due to the tuning parameter involved. Its asymptotic expansions and its optimal sample fraction in terms of minimal asymptotic mean square error are derived. A small scale Monte Carlo simulation turns out that the new estimators, with a suitable choice of the tuning parameter driven by the data itself, perform well compared to the known ones. Finally, the proposed estimators with a bootstrap optimal sample fraction are applied to an environmental data set.
| Original language | English |
|---|---|
| Pages (from-to) | 1166-1176 |
| Number of pages | 11 |
| Journal | Communications in Statistics - Theory and Methods |
| Volume | 48 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 4 Mar 2019 |
| Externally published | Yes |
Keywords
- Bootstrap methodology
- Extreme value index
- Extreme value statistics
- Location-invariant moment-type estimation