Point processes, regular variation and weak convergence 论文
1986Advances in Applied Probability引用 299
Mathematical Approximation and IntegrationPoint processes and geometric inequalitiesBayesian Methods and Mixture Models
摘要
A method is reviewed for proving weak convergence in a function-space setting when regular variation is a sufficient condition. Point processes and weak convergence techniques involving continuity arguments play a central role. The method is dimensionless and holds computations to a minimum. Many applications of the methods to processes derived from sums and maxima are given.