En savoir plus
Ergodic theorems are a cornerstone of the theory of stochastic processes and their applications.
This volume delves into ergodic theorems with explicit power and exponential upper bounds for convergence rates, focusing on Markov chains, renewal processes, and regenerative processes. The book offers a powerful and constructive probabilistic framework by employing the elegant coupling method in conjunction with test functions. Theoretical findings are illustrated with applications to perturbed stochastic networks, alternating Markov processes, risk processes, quasi-stationary distributions, and the renewal theorem, all of which feature explicit convergence rate bounds.
Many results presented here are groundbreaking, appearing in publication for the first time. This is the first volume of a two-volume monograph dedicated to ergodic theorems. While this volume centers on Markovian and regenerative models, the second volume extends the scope to semi-Markov processes and multi-alternating regenerative processes with semi-Markov modulation.
Designed with researchers and advanced students in mind, the content is thoughtfully structured by complexity, making it suitable for self-study or as a resource for upper-level coursework. Each chapter is self-contained and complemented by a comprehensive bibliography, ensuring its value as a long-lasting reference. An essential resource for theoretical and applied research, this book significantly contributes to the field of stochastic processes and will remain a key reference for years to come.
Table des matières
Preface.- Introduction.- Coupling for Random Variables.- Coupling and Ergodic Theorems for Finite Markov Chains.- Coupling and Ergodic Theorems for General Markov Chains.- Hitting Times and Method of Test Functions.- Approaching of Renewal Schemes.- Synchronizing of Shifted Renewal Schemes.- Coupling for Renewal Schemes.- Coupling and Ergodic Theorems for Regenerative Processes.- Uniform Ergodic Theorems for Regenerative Processes.- Generalized Ergodic Theorems for Regenerative Processes.- Coupling and the Renewal Theorem.- Appendix A. Basic Ergodic Theorems for Regenerative Processes.- Appendix B. Methodological and Bibliographical Notes.- References.- Index.
A propos de l'auteur
Dmitrii Silvestrov graduated from Kiev University (1968, Mathematics), Candidate of Science [PhD], (1969, Mathematical Statistics), and Doctor of Science (1972, Mathematical Statistics). Awarded the Prize of the Moscow Mathematical Society (1973) and the Ukrainian Ostrovsky Prize (1977) for work on stochastic processes. Lecturer and Senior Lecturer (1970-1974), Professor (1974-1992, Department of Probability and Mathematical Statistics) and Head of the Statistical Research Centre (1980-1990) at Kiev University. Guest scientist at Umeå University (1991-1992), Senior lecturer at Luleå University of Technology (1992-1994) and at Umeå University (1994-1999). Visiting professor at the Hebrew University of Jerusalem (1993), University of Turku (1998), and University of Rome "La Sapienza" (2015). Professor at the Mälardalen University from 1999 (Emeritus Professor from 2012) and Stockholm University from 2009 (Emeritus Professor from 2016). Member of the editorial boards of the journals "Theory of Probability and Mathematical Statistics" and "Theory of Stochastic Processes". Coordinator of the four EU Tempus Projects. The main research areas are stochastic processes, actuarial and financial mathematics, and statistical software. Author of 13 books and more than 170 research papers. Supervised 22 doctoral students who subsequently obtained PhD degrees.
Résumé
Ergodic theorems are a cornerstone of the theory of stochastic processes and their applications.
This volume delves into ergodic theorems with explicit power and exponential upper bounds for convergence rates, focusing on Markov chains, renewal processes, and regenerative processes. The book offers a powerful and constructive probabilistic framework by employing the elegant coupling method in conjunction with test functions. Theoretical findings are illustrated with applications to perturbed stochastic networks, alternating Markov processes, risk processes, quasi-stationary distributions, and the renewal theorem, all of which feature explicit convergence rate bounds.
Many results presented here are groundbreaking, appearing in publication for the first time. This is the first volume of a two-volume monograph dedicated to ergodic theorems. While this volume centers on Markovian and regenerative models, the second volume extends the scope to semi-Markov processes and multi-alternating regenerative processes with semi-Markov modulation.
Designed with researchers and advanced students in mind, the content is thoughtfully structured by complexity, making it suitable for self-study or as a resource for upper-level coursework. Each chapter is self-contained and complemented by a comprehensive bibliography, ensuring its value as a long-lasting reference. An essential resource for theoretical and applied research, this book significantly contributes to the field of stochastic processes and will remain a key reference for years to come.
