Handbook of Statistics 21: Stochastic Processes: Modeling and Simulation

Edited by

  • D.N. Shanbhag, University of Sheffield, UK
  • C.R. Rao, The Pennsylvania State University, USA

This is a sequel to volume 19 of Handbook of Statistics on Stochastic Processes: Modelling and Simulation.It is concerned mainly with the theme of reviewing and in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour. This volume consists of 23 chapters addressing various topics in stochastic processes. These include, among others, those on manufacturing systems, random graphs, reliability, epidemic modelling, self-similar processes, empirical processes, time series models, extreme value theory, applications of Markov chains, modelling with Monte carlo techniques, and stochastic processes in subjects such as engineering, telecommunications, biology, astronomy and chemistry. (A complete list of the topics addressed in the volume is available from the "Contents" of the volume.)

An attempt is made to cover in this volume, as in the case of its predecessor, as many topics as possible. Among various issuesconsidered in this volume, there are those concerned in particular withmodelling, simulation techniques and numerical methods concerned with stochastic processes. The scope of the project involving this volume as well as volume 19 is already clarified in the "Preface" of volume 19. The presentvolume completes the aim of the project, and it should serve as an aid to students, teachers, researchers and practitioners interested in applied stochastic processes.

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Audience

University and departmental (mathematics and statistics) libraries, pharmaceutical companies, telecommunication industries, medical and engineering schools.

 

Book information

  • Published: February 2003
  • Imprint: NORTH-HOLLAND
  • ISBN: 978-0-444-50013-7

Reviews

This volume ought to be available to all statistical departments, especially applied ones, so that staff and research students are encouraged to realize the importance of applied stochastic processes.
Freda Kemp, St. Andrews University, Journal of the Royal Statistical Society, Vol. 157 (1), 2004



Table of Contents

Chapter 1
Modeling and Numerical Methods in Manufacturing Systems using Control Theory, (E.K.Boukas, Z.K.Liu).
Chapter 2
Models of Random Graphs and their Applications, (C.Cannings, D.B.Penman).
Chapter 3
Locally Self-Similar Processes and their Wavelet Analysis, (J.E.Cavanaugh, Y.Wang, J.W.Davis).
Chapter 4
Stochastic Models for DNA Replications, (R.Cowan).
Chapter 5
An Empirical Process with Applications to Test Exponential and Geometric Models, (J.E.Ferreira).
Chapter 6
Patterns of Sequences of Random Events, (J.Gani).
Chapter 7
Stochastic Models in Telecommunications for Optimal Design, Control, and Performance Evaluation, (N.Gautam).
Chapter 8
Stochastic Processes in Epidemic Modelling and Simulation, (D.Greenhalgh).
Chapter 9
Inference and Simulation for Random Fields, (P.Greenwood, W.Wefelmeyer).
Chapter 10
Modeling Self-Similarity: Fractals and Stochastic Processes, (B.M.Hambly).
Chapter 11
Numerical Methods in Queueing Theory, (D.Heyman).
Chapter 12
Applications of Markov Chains to the Distribution Theory of Runs and Patterns, (M.V.Koutras).
Chapter 13
Modeling Image Analysis Problems using Markov Random Fields, (S.Z.Li).
Chapter 14
Semi-Markov Processes in Reliability, (N.Limnios, G.Oprisan).
Chapter 15
Departures and Related Characteristics in Queueing Models, (M.Manoharan, M.H.Alamatsaz, D.N.Shanbhag).
Chapter 16
Discrete Variate Time Series, (E.D.McKenzie).
Chapter 17
Extreme Value Theory, Models and Simulation, (S.Nadarajah).
Chapter 18
Biological Applications of Branching Processes, (A.G.Pakes).
Chapter 19
Markov Chain Approaches to Damage Models, (C.R.Rao, M.Albassam, M.B.Rao, D.N.Shanbhag).
Chapter 20
Point Processes in Astronomy: Exciting Events in the Universe, (J.D.Scargle, G.J.Babu).
Chapter 21
On the Theory of Discrete and Continuous Parameter Biliniear Random Processes, (T.Subba Rao, G.Terdic).
Chapter 22
Non-Linear and Non-Gaussian State-Space Modeling with Monte Carlo Techniques: A Survey and Comparative Study, (H.Tanizaki).
Chapter 23
Markov Modeling of Burst Behaviour in Ion Channels, (G.F.Yeo, R.K.Milne, B.W.Madsen, Y.Li, R.O.Edeson).