The areal modeling of the extremes of a natural process such as rainfall or temperature is
important in environmental statistics; for example, understanding extreme areal rainfall is …

MODELLING TIME SERIES EXTREMES Page 1 REVSTAT – Statistical Journal Volume 10,
Number 1, March 2012, 109–133 MODELLING TIME SERIES EXTREMES Authors: V …

A new technique based on Bayesian quantile regression that models the dependence of a
quantile of one variable on the values of another using a natural cubic spline is presented …

Extreme value models are typically used to describe the distribution of rare events.
Generally, an asymptotically motivated extreme value model is used to approximate the tail …

Non-stationary modelling of climate extremes has attracted significant attention in recent
years. Generalized extreme value (GEV) distribution is the standard approach for modelling …

Summary We develop Mean Field Variational Bayes methodology for fast approximate
inference in Bayesian Generalized Extreme Value additive model analysis. Such models are …

Leading economic indicators are often used to anticipate changes in key economic
variables. Understanding the dynamics of these indicators is of primary interest for policy …

We propose a novel statistical model to describe spatio-temporal extreme events. The model
can be used, for instance, to estimate extreme-value temporal pattern such as seasonality …

We model extreme river flow data from five UK rivers with distinct hydrological properties.
The data exhibit significant and complex nonstationarity, which we model using a nonlinear …

This study describes the approach for modeling extreme and lengthy time-varying series of
an Autoregressive Moving Average of order (,) pq via a Skew Generalized Extreme Value …