Tail dependence models for distributions attracted to a max-stable law are fitted using observations above a high threshold. To cope with spatial, high-dimensional data, a rank based M-estimator is proposed relying on bivariate margins only. A data-driven weight matrix is used to minimize the...

City size distributions are known to be well approximated by power laws across many countries. One popular explanation for such power-law regularities is in terms of random growth processes, where power laws arise asymptotically from the assumption of iid growth rates among all cities within a...

Measuring the spatial distribution of locations of many entities (trees, atoms, economic activities, etc.), and, more precisely, the deviations from purely random configurations, is a powerful method to unravel their underlying interactions. Several coefficients have been developed in the past...

An avalanche of empirical studies has addressed the validity of the rank-size rule (or Zipf's law) in a multi-city context in many countries. City size in most countries seems to obey Zipf's law, but the question under which conditions (e.g. sample size, spatial scale) this 'law' holds remained...