Potential Analysis of Stable Processes and its Extensions


Book Description

Stable Lévy processes and related stochastic processes play an important role in stochastic modelling in applied sciences, in particular in financial mathematics. This book is about the potential theory of stable stochastic processes. It also deals with related topics, such as the subordinate Brownian motions (including the relativistic process) and Feynman–Kac semigroups generated by certain Schrödinger operators. The authors focus on classes of stable and related processes that contain the Brownian motion as a special case. This is the first book devoted to the probabilistic potential theory of stable stochastic processes, and, from the analytical point of view, of the fractional Laplacian. The introduction is accessible to non-specialists and provides a general presentation of the fundamental objects of the theory. Besides recent and deep scientific results the book also provides a didactic approach to its topic, as all chapters have been tested on a wide audience, including young mathematicians at a CNRS/HARP Workshop, Angers 2006. The reader will gain insight into the modern theory of stable and related processes and their potential analysis with a theoretical motivation for the study of their fine properties.




Stable Processes and Related Topics


Book Description

The Workshop on Stable Processes and Related Topics took place at Cor nell University in January 9-13, 1990, under the sponsorship of the Mathemat ical Sciences Institute. It attracted an international roster of probabilists from Brazil, Japan, Korea, Poland, Germany, Holland and France as well as the U. S. This volume contains a sample of the papers presented at the Workshop. All the papers have been refereed. Gaussian processes have been studied extensively over the last fifty years and form the bedrock of stochastic modeling. Their importance stems from the Central Limit Theorem. They share a number of special properties which facilitates their analysis and makes them particularly suitable to statistical inference. The many properties they share, however, is also the seed of their limitations. What happens in the real world away from the ideal Gaussian model? The non-Gaussian world may contain random processes that are close to the Gaussian. What are appropriate classes of nearly Gaussian models and how typical or robust is the Gaussian model amongst them? Moving further away from normality, what are appropriate non-Gaussian models that are sufficiently different to encompass distinct behavior, yet sufficiently simple to be amenable to efficient statistical inference? The very Central Limit Theorem which provides the fundamental justifi cation for approximate normality, points to stable and other infinitely divisible models. Some of these may be close to and others very different from Gaussian models.










Stochastic Processes and Related Topics


Book Description

Spectral Representation and Structure of Stable Self-Similar Processes.- Three Elementary Proofs of the Central Limit Theorem with Applications to Random Sums.- Almost Everywhere Convergence and SLLN Under Rearrangements.- Sufficient Conditions for the Existence of Conditional Moments of Stable Random Variables.- How Heavy are the Tails of a Stationary HARCH(k) Process? A Study of the Moments.- Use of Stochastic Comparisons in Communication Networks.- On the Conditional Variance-Covariance of Stable Random Vectors, II.- Interacting Particle Approximation for Fractal Burgers Equation.- Optimal Transformations for Prediction in Continuous-Time Stochastic Processes.- Algebraic Methods Toward Higher-Order Probability Inequalities.- Comparison and Deviation from a Representation Formula.- Components of the Strong Markov Property.- The Russian Options.- Cycle Representations of Markov Processes: An Application to Rotational Partitions.- On Extreme Values in Stationary Random Fields.- Norming Operators for Operator-Self-Similar Processes.- Multivariate Probability Density and Regression Functions Estimation of Continuous-time Stationary Processes from Discrete-time Data.- Tracing the Path of a Wright-Fisher Process with One-way Mutation in the Case of a Large Deviation.- A Distribution Inequality for Martingales with Bounded Symmetric Differences.- Moment Comparison of Multilinear Forms in Stable and Semistable Random Variables with Application to Semistable Multiple Integrals.- Global Dependency Measure for Sets of Random Elements: "The Italian Problem" and Some Consequences.




Stable Processes and Related Topics


Book Description




Stochastic Processes and Related Topics


Book Description

In the last twenty years extensive research has been devoted to a better understanding of the stable and other closely related infinitely divisible mod els. Stamatis Cambanis, a distinguished educator and researcher, played a special leadership role in the development of these research efforts, particu larly related to stable processes from the early seventies until his untimely death in April '95. This commemorative volume consists of a collection of research articles devoted to reviewing the state of the art of this and other rapidly developing research and to explore new directions of research in these fields. The volume is a tribute to the Life and Work of Stamatis by his students, friends, and colleagues whose personal and professional lives he has deeply touched through his generous insights and dedication to his profession. Before the idea of this volume was conceived, two conferences were held in the memory of Stamatis. The first was organized by the University of Athens and the Athens University of Economics and was held in Athens during December 18-19, 1995. The second was a significant part of a Spe cial IMS meeting held at the campus of the University of North Carolina at Chapel Hill during October 17-19, 1996. It is the selfless effort of sev eral people that brought about these conferences. We believe that this is an appropriate place to acknowledge their effort; and on behalf of all the participants, we extend sincere thanks to all these persons.




Stable Non-Gaussian Random Processes


Book Description

This book serves as a standard reference, making this area accessible not only to researchers in probability and statistics, but also to graduate students and practitioners. The book assumes only a first-year graduate course in probability. Each chapter begins with a brief overview and concludes with a wide range of exercises at varying levels of difficulty. The authors supply detailed hints for the more challenging problems, and cover many advances made in recent years.