Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909717934112768 |
|---|---|
| author | McVittie, James H. Murphy, Orla A. |
| author_facet | McVittie, James H. Murphy, Orla A. |
| contents | Missing data occur in a variety of applications of extreme value analysis. In the block maxima approach to an extreme value analysis, missingness is often handled by either ignoring missing observations or dropping a block of observations from the analysis. However, in some cases, missingness may occur due to equipment failure during an extreme event, which can lead to bias in estimation. In this work, we propose weighted maximum likelihood and weighted moment-based estimators for the generalized extreme value distribution parameters to account for the presence of missing observations. We validate the procedures through an extensive simulation study and apply the estimation methods to data from multiple tidal gauges on the Eastern coast of Canada. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations McVittie, James H. Murphy, Orla A. Methodology Applications 62P12 Missing data occur in a variety of applications of extreme value analysis. In the block maxima approach to an extreme value analysis, missingness is often handled by either ignoring missing observations or dropping a block of observations from the analysis. However, in some cases, missingness may occur due to equipment failure during an extreme event, which can lead to bias in estimation. In this work, we propose weighted maximum likelihood and weighted moment-based estimators for the generalized extreme value distribution parameters to account for the presence of missing observations. We validate the procedures through an extensive simulation study and apply the estimation methods to data from multiple tidal gauges on the Eastern coast of Canada. |
| title | Weighted Parameter Estimators of the Generalized Extreme Value Distribution in the Presence of Missing Observations |
| topic | Methodology Applications 62P12 |
| url | https://arxiv.org/abs/2506.15964 |