Semi-Markov Models and Applications

  • Jacques Janssen
  • Nikolaos Limnios

Table of contents

  1. Front Matter
    Pages i-xxi
  2. Extensions of Basic Models

    1. Front Matter
      Pages 1-1
    2. Marius Iosifescu
      Pages 23-32
    3. Mats Gyllenberg, Dmitrii S. Silvestrov
      Pages 33-60
    4. Vladimir S. Korolyuk
      Pages 61-75
  3. Statistical Estimation

    1. Front Matter
      Pages 117-117
    2. George G. Roussas, Debasis Bhattacharya
      Pages 149-166
    3. Valeri T. Stefanov
      Pages 167-186
    4. Larisa Afanasyeva, Peter Radchenko
      Pages 187-199
    5. Ilya Gertsbakh, I. Spungin
      Pages 201-206
    6. Brahim Ouhbi, Nikolaos Limnios
      Pages 207-218
    7. Sally McClean, Erin Montgomery
      Pages 219-227
    8. Rafael Pérez-Ocón, Juan Eloy Ruiz-Castro, M. Luz Gámiz-Pérez
      Pages 229-238
  4. Non-Homogeneous Models

    1. Front Matter
      Pages 239-239
    2. Aleka A. Papadopoulou, Panagiotis C. G. Vassiliou
      Pages 241-251
    3. Panagiotis C. G. Vassiliou, Helena Tsakiridou
      Pages 253-265

About this book

Introduction

This book presents a selection of papers presented to the Second Inter­ national Symposium on Semi-Markov Models: Theory and Applications held in Compiegne (France) in December 1998. This international meeting had the same aim as the first one held in Brussels in 1984 : to make, fourteen years later, the state of the art in the field of semi-Markov processes and their applications, bring together researchers in this field and also to stimulate fruitful discussions. The set of the subjects of the papers presented in Compiegne has a lot of similarities with the preceding Symposium; this shows that the main fields of semi-Markov processes are now well established particularly for basic applications in Reliability and Maintenance, Biomedicine, Queue­ ing, Control processes and production. A growing field is the one of insurance and finance but this is not really a surprising fact as the problem of pricing derivative products represents now a crucial problem in economics and finance. For example, stochastic models can be applied to financial and insur­ ance models as we have to evaluate the uncertainty of the future market behavior in order, firstly, to propose different measures for important risks such as the interest risk, the risk of default or the risk of catas­ trophe and secondly, to describe how to act in order to optimize the situation in time. Recently, the concept of VaR (Value at Risk) was "discovered" in portfolio theory enlarging so the fundamental model of Markowitz.

Keywords

calculus data analysis estimator likelihood Markov model Markov process modeling Optimal control Power Random Walk stability stochastic process stochastic processes systems theory Value at Risk

Editors and affiliations

  • Jacques Janssen
    • 1
  • Nikolaos Limnios
    • 2
  1. 1.Université Libre de BruxellesBelgium
  2. 2.Université de Technologie de CompiègneFrance

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-3288-6
  • Copyright Information Springer-Verlag US 1999
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-3290-9
  • Online ISBN 978-1-4613-3288-6
  • About this book