Overview

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

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9783110475425

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Reviews

"""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"

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

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

Author Information

Vidyadhar Mandrekar, Michigan State University, USA.

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