Temporal evolution of the extreme excursions of multivariate kth order Markov processes with application to oceanographic data

02/28/2023
by   Stan Tendijck, et al.
0

We develop two models for the temporal evolution of extreme events of multivariate kth order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan Tawn (2004), and it naturally extends the work of Winter Tawn (2016,2017) and Tendijck et al. (2019) to include multivariate random variables. We use cross-validation-type techniques to develop a model order selection procedure, and we test our models on two-dimensional meteorological-oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly-developed models perform better than the widely used historical matching methodology for these data.

READ FULL TEXT
research
08/01/2023

Unified unconditional regression for multivariate quantiles, M-quantiles and expectiles

In this paper, we develop a unified regression approach to model uncondi...
research
07/29/2022

Modelling multivariate extreme value distributions via Markov trees

Multivariate extreme value distributions are a common choice for modelli...
research
05/04/2019

Regularized estimation for highly multivariate log Gaussian Cox processes

Statistical inference for highly multivariate point pattern data is chal...
research
12/19/2022

Detecting causal covariates for extreme dependence structures

Determining the causes of extreme events is a fundamental question in ma...
research
09/22/2022

Standardisation overcomes counter-examples of conditional extremes

A key aspect where extreme values methods differ from standard statistic...
research
07/21/2023

A multi-modal representation of El Niño Southern Oscillation Diversity

The El Niño-Southern Oscillation (ENSO) is characterized by alternating ...

Please sign up or login with your details

Forgot password? Click here to reset