The statistical theory of extremes is extended to observations that are non-stationary and not independent. The non-stationarity over time and space is controlled via the scedasis (tail scale) in the marginal distributions. Spatial dependence stems from multivariate extreme value theory. We establish asymptotic theory for both the weighted sequential tail empirical process and the weighted tail quantile process based on all observations, taken over time and space. The results yield two statistical tests for homoscedasticity in the tail, one in space and one in time. Further, we show that the common extreme value index can be estimated via a pseudo-maximum likelihood procedure based on pooling all (non-stationary and dependent) observations. Our leading example and application is rainfall in Northern Germany.

Multivariate extreme value statistics, non-identical distributions, sequential tail empirical process, testing.
Annals of Statistics
Department of Econometrics

Einmahl, J.H.J, Ferreira, A, de Haan, L, Neves, C., & Zhou, C. (2021). Spatial dependence and space-time trend in extreme events. Annals of Statistics, accepted. Retrieved from