Extreme Values Approach in Food Risk Modeling

Spatial modeling for risk assessment of extreme values from environmental time series: A Bayesian nonparametric approach

We propose an approach to modeling and risk assessment for extremes of environmental processes evolving over time and recorded at a number of spatial locations. We follow an extension of the point process approach to analysis of extremes under which the times of exceedances over a given threshold are assumed to arise from a non-homogeneous Poisson process. To achieve flexible shapes and tempora...

A conditional approach for multivariate extreme values

Multivariate extreme value theory and methods concern the characterization, estimation and extrapolation of the joint tail of the distribution of a d-dimensional random variable. Existing approaches are based on limiting arguments in which all components of the variable become large at the same rate.This limit approach is inappropriate when the extreme values of all the variables are unlikely t...

An Introduction to Statistical Modeling of Extreme Values

Bayesian analysis of extreme values by mixture modeling

Modeling of extreme values in the presence of heterogeneity is still a relatively unexplored area. We consider losses pertaining to several related categories. For each category, we view exceedances over a given threshold as generated by a Poisson process whose intensity is regulated by a specific location, shape and scale parameter. Using a Bayesian approach, we develop a hierarchical mixture ...

Free Extreme Values

Free probability analogues of the basics of extreme value theory are obtained, based on Ando’s spectral order. This includes classification of freely max-stable laws and their domains of attraction, using “free extremal convolutions” on the distributions. These laws coincide with the limit laws in the classical peaks-over-threshold approach. A free extremal projection-valued process over a meas...