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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 processesWeak convergence in metric spacesWeak convergence on C[0, 1] and D[0, )Central limit theorem for semi-martingales and applicationsCentral limit theorems for dependent random variablesEmpirical processBibliography

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"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

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Vidyadhar Mandrekar, Michigan State University, USA.

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