香蕉视频

Title:聽Exceedance-based nonlinear regression of tail dependence.

Abstract:聽The probability and structure of co-occurrences of extreme values in multivariate data may critically depend on auxiliary information provided by covariates. In this talk, I will develop a flexible generalized additive modelling framework based on high threshold exceedances for estimating covariate-dependent joint tail characteristics for regimes of asymptotic dependence and asymptotic independence. The framework is based on suitably defined marginal pretransformations and projections of the random vector along the directions of the unit simplex, which lead to convenient univariate representations of multivariate exceedances based on the exponential distribution. We illustrate this modelling framework on a large dataset of nitrogen dioxide measurements recorded in France between 1999 and 2012, where we use the generalized additive framework for modelling marginal distributions and tail dependence in monthly maxima. Results imply asymptotic independence of data observed at different stations. We find that the estimated coefficients of tail dependence decrease as a function of spatial distance. Differences further arise in the patterns for different years and for different types of stations (traffic vs. background).