Modeling spatial tail dependence with Cauchy convolution processes

Modeling tail dependence using copulas — literature review

Some Topics in Convolution-Based Spatial Modeling

Over the last decade, convolution-based models for spatial data have increased in popularity as a result of their flexibility in modeling spatial dependence and their ability to accommodate large datasets. The modeling flexibility is due to the framework’s moving-average construction that guarantees a valid (i.e., non-negative definite) spatial covariance function. This constructive approach to...

Modeling Spatial Organization with Swarm Intelligence Processes

Urban Dynamics modeling needs to implement spatial organization emergence in order to describe the development of services evolution and their usage within spatial centers. In this paper, we propose an extension of the nest building algorithm with multi-center, multi-criteria and adaptive processes. We combine a decentralized approach based on emergent clustering mixed with spatial constraints ...

A Generalized Convolution Model for Multivariate Nonstationary Spatial Processes

We propose a constructive method for specifying flexible classes of nonstationary stochastic models for multivariate spatial data. The method is based upon convolutions of spatially varying covariance functions and produces mathematically valid covariance structures. This method generalizes the convolution approach suggested by Majumdar and Gelfand (2007) to extend multivariate spatial covarian...

A parsimonious multivariate copula for tail dependence modeling

Copulas are increasingly studied both in theory and practice as they are a convenient tool to construct multivariate distribution functions. However the material essentially covers the bi-variate case while in applications the number of variables is much higher. Furthermore, when one wants to take into account tail dependence, a desirable property is to have enough flexibility in the tails whil...