The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion.
Contents:
Weak convergence of stochastic processes
Weak convergence in metric spaces
Weak convergence on C[0, 1] and D[0, )
Central limit theorem for semi-martingales and applications
Central limit theorems for dependent random variables
Empirical process
Bibliography
Vidyadhar Mandrekar, Michigan State University, USA. "Written by an expert in probability theory and stochastic processes, the book succeeds to present, in a relatively small number of pages, some fundamental results on weak convergence in probability theory and stochastic process and applications."
Hannelore Lisei in: Stud. Univ. Babes-Bolyai Math. 62(2017), No. 1, 137-138
