Weak Convergence of Stochastic Processes: With Applications to Statistical Limit Theorems

✍ Scribed by Vidyadhar S. Mandrekar

Publisher: De Gruyter
Year: 2016
Tongue: English
Leaves: 148
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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

✦ 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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