Nonlinear and Nonstationary Signal Processing
Nonlinear and Nonstationary Signal Processing
R. L. Smith, University of North Carolina, Chapel Hill
A. T. Walden, Imperial College of Science, Technology and Medicine, London
P. C. Young, Lancaster University
Out of Print
Most currently employed methods that are used in various fields of data analysis, such as signal processing and time series analysis, are based on rather simplistic assumptions about the linearity and stationarity of the underlying processes, and are hence suboptimal in many situations. The chapters in this book introduce modern methods that have been developed in many fields of statistics, engineering, environmental science or finance, to address these shortcomings. The authors present state-of-the-art statistical methods, and discuss their applications in real-world situations. The chapters, taken together, provide a coherent and unique account of this active and important area, that will appeal to graduate students and researchers in academia, industry and laboratories.
- Brings together modern techniques that can be applied to many different fields
- State of the art statistical methods and unification of them
- Many different application areas
Reviews & endorsements
'… is a useful progress report for anyone seeking to go beyond the basics, and a good addition to the comparatively small literature on non-stationary and nonlinear processes.' Carl Wunsch, EOS
'I have no doubt many readers will find this volume useful.' T. Subba Rao, Publication of the International Statistical Institute
'… will certainly be welcomed by those wishing to understand and apply the latest techniques in this area.' Alex M. Andrew, Robotica
Product details
January 2002Hardback
9780521800440
484 pages
255 × 181 × 32 mm
1.369kg
5 colour illus. 8 tables
Unavailable - out of print
Table of Contents
- Introduction
- 1. Bayesian computational approaches to model selection C. Andrieu, A. Doucet, W. J. Fitzgerald and J. -M. Pérez
- 2. Sequential analysis of nonlinear dynamic systems using particles and mixtures Neil Gordon, Alan Marrs and David Salmond
- 3. Stochastic, dynamic modelling and signal processing: time variable and state dependent parameter estimation Peter Young
- 4. The use of generalised likelihood measures for uncertainty estimation in high-order models of environmental systems Keith Beven, Jim Freer, Barry Hankin and Karsten Schulz
- 5. Spatial statistics in environmental science Richard L. Smith
- 6. Useful lies: dynamics from data Alistair Mees
- 7. A modelling framework for the prices and times of trades made on the New York Stock Exchange Tina Hviid Rydberg and Neil Shephard
- 8. The sample autocorrelations of financial Time Series Models Richard A. Davis and Thomas Mikosch
- 9. The many roads to time-frequency Patrick Flandrin
- 10. Multiple Window time-varying spectrum estimation Metin Bayram and Richard Baraniuk
- 11. Multitaper analysis of nonstationary and nonlinear Time Series Data David J. Thomson
- 12. Signal and image denoising via wavelet thresholding: orthogonal and biorthogonal, scalar and multiple wavelet transforms Vasily Strela and Andrew Walden
- 13. Wavestrapping time series: adaptive wavelet-based bootstrapping D. B. Percival, S. Sardy and A. C. Davison.
