Reviews:
''The book will be very useful both for applications and
for further development of this important area.' Biometrics
'For mathematical statisticians with a good advance knowledge
of diffusion theory, the review character of the book would
be useful and point the reader to the relevant literature.'
Statistics in Medicine
Key Features:
- Leading author in this field
- Contains examples and exercises
- Looks at new application areas for this branch of probability.
Description:
Statistical inference for stochastic processes is of great
importance from a theoretical as well as from an applications
point of view in model building. During the past 20 years,
there has been a large amount of progress in the study of
time stochastic processes. Diffusion type processes is a large
class of continuous time processes which are widely used for
stochastic modelling. This book aims to bring together several
methods of estimation of parameters involved in such processes
when the process is observed continuously over a period of
time or when sampled data is available. It discusses parametric
as well as nonparametric aspects of the problem.
Readership:
Graduate statisticians and researchers in statistics and
applied probability.
Contents:
Preface
Introductory notes
1. Diffusion type processes
2. Parametric inference for diffusion type processes from continuous
paths
3. Parametric inference for diffusion type processes from sampled
data
4. Nonparametric inference for diffusion type processes from
continuous paths
5. Nonparametric inference for diffusion type processes from
sampled data
6. Applications to stochastic modelling
7. Numerical approximation methods for stochastic differential
equations
Appendix A Uniform ergodic theorem
Appendix B Stochastic integration and limit theorems for stochastic
integrals
Appendix C Wavelets
Appendix D Gronwall-Bellman type lemma
References
Author index
Subject index |
