A0674
Title: Accounting for missing data when modelling block maxima
Authors: Emma Simpson - University College London (United Kingdom) [presenting]
Paul Northrop - University College London (United Kingdom)
Abstract: Modeling block maxima using the generalized extreme value (GEV) distribution is a classical and widely used method for studying univariate extremes. It allows for theoretically motivated estimation of return levels, including extrapolation beyond the range of observed data. A frequently overlooked challenge in applying this methodology comes from handling datasets containing missing values. In this case, one cannot be sure whether the true maximum has been recorded in each block, and simply ignoring the issue can lead to biased parameter estimators and underestimated return levels. An extension of the standard block maxima approach is proposed to overcome such missing data issues. This is achieved by explicitly accounting for the proportion of missing values in each block within the GEV model. Inference is carried out using likelihood-based techniques, and an update is proposed to commonly used diagnostic plots to assess model fit. The performance of the method is assessed via a simulation study, with results that are competitive with the "ideal" case of having no missing values. The methodology is further demonstrated on ozone data from Plymouth, U.K.
