Ebook: Time Series Analysis and Applications to Geophysical Systems: Part I

Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.

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Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.

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Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.
Content:
Front Matter....Pages i-xii
Nonparametric Deconvolution of Seismic Depth Phases....Pages 1-10
State Space Approach to Signal Extraction Problems in Seismology....Pages 11-39
Improved Signal Transmission through Randomization....Pages 41-51
Online Analysis of Seismic Signals....Pages 53-71
Nonstationary Time Series Analysis of Monthly Global Temperature Anomalies....Pages 73-103
A Test for Detecting Changes in Mean....Pages 105-121
Spatio-temporal Modelling of Temperature Time Series: A Comparative Study....Pages 123-150
Modeling North Pacific Climate Time Series....Pages 151-167
Skew-elliptical Time Series with Application to Flooding Risk....Pages 169-185
Hidden Periodicities Analysis and Its Application in Geophysics....Pages 187-194
The Innovation Approach to the Identification of Nonlinear Causal Models in Time Series Analysis....Pages 195-226
Non-Gaussian Time Series Models....Pages 227-237
Modeling Continuous Time Series Driven by Fractional Gaussian Noise....Pages 239-255
Back Matter....Pages 257-260

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Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.
Content:
Front Matter....Pages i-xii
Nonparametric Deconvolution of Seismic Depth Phases....Pages 1-10
State Space Approach to Signal Extraction Problems in Seismology....Pages 11-39
Improved Signal Transmission through Randomization....Pages 41-51
Online Analysis of Seismic Signals....Pages 53-71
Nonstationary Time Series Analysis of Monthly Global Temperature Anomalies....Pages 73-103
A Test for Detecting Changes in Mean....Pages 105-121
Spatio-temporal Modelling of Temperature Time Series: A Comparative Study....Pages 123-150
Modeling North Pacific Climate Time Series....Pages 151-167
Skew-elliptical Time Series with Application to Flooding Risk....Pages 169-185
Hidden Periodicities Analysis and Its Application in Geophysics....Pages 187-194
The Innovation Approach to the Identification of Nonlinear Causal Models in Time Series Analysis....Pages 195-226
Non-Gaussian Time Series Models....Pages 227-237
Modeling Continuous Time Series Driven by Fractional Gaussian Noise....Pages 239-255
Back Matter....Pages 257-260
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