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Mandrekar, Vidyadhar S. Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems. — Berlin/Boston, GERMANY: De Gruyter, 2016. — 1 online resource (148). — (De Gruyter Textbook). — <URL:http://elib.fa.ru/ebsco/1362715.pdf>.Record create date: 10/7/2016 Subject: Limit theorems (Probability theory); Fourier transformations.; Stochastic processes.; Fourier transformations.; Limit theorems (Probability theory); Stochastic processes.; MATHEMATICS / Applied; MATHEMATICS / Probability & Statistics / General Collections: EBSCO Allowed Actions: –
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Annotation
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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Table of Contents
- Contents
- 1. Weak convergence of stochastic processes
- 2. Weak convergence in metric spaces
- 3. Weak convergence on C[0, 1] and D[0,8)
- 4. Central limit theorem for semi-martingales and applications
- 5. Central limit theorems for dependent random variables
- 6. Empirical process
- Bibliography
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