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.
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Most currently employed methods that are used in various fields of data analysis are based on rather simplistic assumptions about linearity and stationarity, 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.
"...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 non-linear processes. Scientists interested in pursuing this subject will find it helpful." EOS
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