# Bayesian inference for generalized extreme value distribution with Gaussian copula dependence

@article{Ning2017BayesianIF, title={Bayesian inference for generalized extreme value distribution with Gaussian copula dependence}, author={Bo Ning and Peter R. Bloomfield}, journal={arXiv: Methodology}, year={2017} }

Dependent generalized extreme value (dGEV) models have attracted much attention due to the dependency structure that often appears in real datasets. To construct a dGEV model, a natural approach is to assume that some parameters in the model are time-varying. A previous study has shown that a dependent Gumbel process can be naturally incorporated into a GEV model. The model is a nonlinear state space model with a hidden state that follows a Markov process, with its innovation following a Gumbel…

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