Spatial dependence and space–time trend in extreme events
| Autor: | John H. J. Einmahl, Ana Ferreira, Laurens de Haan, Cláudia Neves, Chen Zhou |
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| Přispěvatelé: | Econometrics and Operations Research, Research Group: Econometrics, Econometrics |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | The Annals of Statistics, 50(1), 30-52. Institute of Mathematical Statistics Annals of Statistics, 50(1):50(1), 30-52. Institute of Mathematical Statistics |
| ISSN: | 0090-5364 2168-8966 |
| DOI: | 10.1214/21-aos2067 |
| Popis: | 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. Comment: Supporting information: the detailed proof of Theorem 6, referenced in Section 4, as well as simulations showcasing finite sample performance of the proposed methods are available with this paper at https://bit.ly/3aJFM6B |
| Databáze: | OpenAIRE |
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