Semiparametric approaches for the inference of univariate and multivariate extremes

Abstract

In this paper, we present several semiparametric approaches for the inference of univariate and multivariate extremes to resolve the tasks from the Data Challenge at the 13th Conference on Extreme Value Analysis. We implement generalized additive models to capture the fexible relationship for point and interval estimations of the conditional quantiles. We also adopt $L^p$-quantile to estimate the marginal quantiles of extreme levels. To predict probabilities of multivariate extreme events, we implement conditional methods by Heffernan and Tawn (2004) and Keef et al. (2013). When estimating the excess probability of 50-dimensional data, we cluster variables with high correlation after simple data exploration and combine the results obtained from each cluster. We further validate predicted models based on cross-validation and select the best estimates to achieve high accuracy.

Contribution

We achieved second place in the Extreme Value Analysis 2023 Data Competition.

Code

To be uploaded.