Statistical Topics and Stochastic Models for Dependent Data with Applications

This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.

Vlad Stefan Barbu is Associate Professor in Statistics with LMRS at the University of Rouen Normandy, France. His main research focuses on statistics of stochastic processes and on techniques based on divergence measures, with a particular interest in semi-Markov and hidden semi-Markov processes.

Nicolas Vergne is Associate Professor in Statistics with LMRS at the University of Rouen Normandy. His research work is in statistics, focusing on different Markov-type models: drifting Markov models, semi-Markov models, hidden Markov models and bioinformatics.