
Markov Chains with Asymptotically Zero Drift: Lamperti’s Problem (New Mathematical Monographs, Series Number 51)
Author(s): Denis Denisov (Author), Dmitry Korshunov (Author), Vitali Wachtel (Author)
- Publisher Finelybook 出版社: Cambridge University Press
- Publication Date 出版日期: May 8, 2025
- Language 语言: English
- Print length 页数: 428 pages
- ISBN-10: 1009554220
- ISBN-13: 9781009554220
Book Description
This text examines Markov chains whose drift tends to zero at infinity, a topic sometimes labelled as ‘Lamperti’s problem’. It can be considered a subcategory of random walks, which are helpful in studying stochastic models like branching processes and queueing systems. Drawing on Doob’s h-transform and other tools, the authors present novel results and techniques, including a change-of-measure technique for near-critical Markov chains. The final chapter presents a range of applications where these special types of Markov chains occur naturally, featuring a new risk process with surplus-dependent premium rate. This will be a valuable resource for researchers and graduate students working in probability theory and stochastic processes.
Editorial Reviews
Editorial Reviews
Book Description
A comprehensive presentation of results for time-homogeneous Markov chains with asymptotically zero drift.
About the Author
Denis Denisov is Reader in Probability in the Department of Mathematics at the University of Manchester. His research interests include multidimensional random walks, ordered and conditioned random walks, Markov chains and diffusion processes.
Dmitry Korshunov is Professor in the School of Mathematical Sciences at Lancaster University. He is an expert in stochastic processes, Markov chains, large deviation theorems, heavy tail phenomena, and limit theorems. He previously co-authored An Introduction to Heavy-Tailed and Subexponential Distributions (2011, 2013).
Vitali Wachtel is Professor for Mathematics at the University of Bielefeld. His research interests include, besides Markov chains, exit times and conditional distributions for multidimensional random walks, branching processes and large deviations.
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