Functional Gaussian Approximation for Dependent Structures (Oxford Studies in Probability)

This book has its origin in the need of developing and analysing mathematical models for phenomena that evolve in time and influence each another, and aims at a better understanding of the structure and asymptotic behaviour of stochastic processes.

Florence Merlevede, Professor, Universite Paris-Est Marne-La-Vallee,Magda Peligrad, Professor, University of Cincinnati,Sergey Utev, University of Leicester, Professor

Florence Merlevede is Professor at the Laboratory of Analysis and Applied Mathematics of the University of Paris-Est. Her main research interests are in moment inequalities, deviations probability inequalities, limit theorems for partial sums associated to dependent processes, empirical processes but include also dynamical systems and random matrices

Magda Peligrad is a distinguished Taft Professor in the Department of Mathematical Sciences of the University of Cincinnati whose area of expertise is Probability Theory and Stochastic Processes. Her research deals with dependent structures and covers various aspects of modelling the dependence, maximal inequalities, and limit theorems. Her research was rewarded by numerous National Science Foundation, National Security Agency, and Taft research center grants. In 1995 she was elected as fellow of the Institute of Mathematical Statistics, in 2003 she received the title of Taft Professor at the University of Cincinnati and in 2010 her contributions to Probability theory were recognized in a meeting held in her honor in Paris, France

Sergey Utev is Professor in the Department of Mathematics of the University of Leicester. His area of expertise covers many aspects of Probability theory and their applications. In particular he wrote many important papers concerning mathematical inequalities and their applications, Quantum probability and stochastic comparisons, Stochastic processes with applications to Financial Mathematics, Actuarial Sciences and Epidemics