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 the fields of statistics, engineering, environmental science and finance, to address these shortcomings. The authors present state-of-the-art statistical methods, and discuss their applications in real-world situations. This volume provides a coherent and unique account of this active and important area.
A text containing a collection of articles introducing modern methods that have been developed in statistics, engineering, finance, environmental science, and other fields, which address the shortcomings of most currently employed methods used in data analysis. For graduate students and researchers in academia, industry, and laboratories.
Signal processing, nonlinear data analysis, nonlinear time series, nonstationary processes.| Contributors |
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vii | |
| Introduction |
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ix | |
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Bayesian Computational Approaches to Model Selection |
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1 | (41) |
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Sequential Analysis of Nonlinear Dynamic Systems using Particles and Mixtures |
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42 | (32) |
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Stochastic, Dynamic Modelling and Signal Processing: Time Variable and State Dependent Parameter Estimation |
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74 | (41) |
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The Use of Generalised Likelihood Measures for Uncertainty Estimation in High-Order Models of Environmental Systems |
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115 | (37) |
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Spatial Statistics in Environmental Science |
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152 | (32) |
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Useful Lies: Dynamics from Data |
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184 | (33) |
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A Modelling Framework for the Prices and Times of Trades made on the New York Stock Exchange |
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217 | (30) |
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The Sample Autocorrelations of Financial Time Series Models |
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247 | (28) |
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The Many Roads to Time-Frequency |
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275 | (17) |
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Multiple Window Time-Varying Spectrum Estimation |
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292 | (25) |
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Multitaper Analysis of Nonstationary and Nonlinear Time Series Data |
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317 | (78) |
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Signal and Image Denoising via Wavelet Thresholding: Orthogonal and Biorthogonal, Scalar and Multiple Wavelet Transforms |
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395 | (47) |
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Wavestrapping Time Series: Adaptive Wavelet-Based Bootstrapping |
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442 | |
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