2025/06/19 by McVittie, James H., Murphy, Orla A. · 1 citation
Earth and Planetary Sciences · Economics, Econometrics and Finance · Environmental Science · #62P12 #Applications (stat.AP) #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Hydrology and Drought Analysis #Methodology (stat.ME) #Ocean Waves and Remote Sensing
paper · pdf · doi:10.48550/arxiv.2506.15964
openalex publication_date 2025/06/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.