This volume covers theoretical, numerical and applied aspects of stochastic processes and stochastic differential equations. The study is motivated in part by the need to model, understand, forecast and control the behavior of many natural phenomena that evolve randomly in time.

Selected papers submitted by participants of the international Conference S tochastic A nalysis and A pplied P robability 2010 ( www.saap2010.org ) make up the basis of this volume.

The SAAP 2010 was held in Tunisia, from 7-9 October, 2010, and was organized by the Applied Mathematics & Mathematical Physics research unit of the preparatory institute to the military academies of Sousse (Tunisia), chaired by Mounir Zili.

The papers cover theoretical, numerical and applied aspects of stochastic processes and stochastic differential equations. The study of such topic is motivated in part by the need to model, understand, forecast and control the behavior of many natural phenomena that evolve in time in a random way. Such phenomena appear in the fields of finance, telecommunications, economics, biology, geology, demography, physics, chemistry, signal processing and modern control theory, to mention just a few.

As this book emphasizes the importance of numerical and theoretical studies of the stochastic differential equations and stochastic processes, it will be useful for a wide spectrum of researchers in applied probability, stochastic numerical and theoretical analysis and statistics, as well as for graduate students.

To make it more complete and accessible for graduate students, practitioners and researchers, the editors Mounir Zili and Daria Filatova have included a survey dedicated to the basic concepts of numerical analysis of the stochastic differential equations, written by Henri Schurz.

Reference for researches in applied probability, stochastic numerical and theoretical analysis and statistics Useful for graduate students Includes supplementary material: sn.pub/extras

Inhalt

Preface.- 1. H. Schurz : Basic Concepts of Numerical Analysis of Stochastic Differential Equations Explained by Balanced Implicit Theta Methods .- 2. C.A. Tudor : Kernel Density Estimation, Local Time and Chaos Expansion.- 3. W. Jedidi, J. Almhana, V. Choulakian, R. McGorman : General Shot Noise Processes and Functional Convergence to Stable Processes.- 4. C. El-Nouty : The Lower Classes of the Sub-Fractional Brownian Motion.- 5. M. Erraoui and Y. Ouknine : On the Bounded Variation of the Flow of Stochastic Differential Equation.- 6. A. Ayache, Q. Peng : Stochastic Volatility and Multifractional Brownian Motion.- 7. A. Gulisashvili, J. Vives : Two-sided Estimates for Distribution Densities in Models with Jumps.- 8. M. Lefebvre : Maximizing a Function of the Survival Time of a Wiener Process in an Interval.