Title	Time Series Analysis and Applications to Geophysical Systems [electronic resource] : Part I / edited by David R. Brillinger, Enders Anthony Robinson, Frederic Paik Schoenberg
Imprint	New York, NY : Springer New York, 2004
Connect to	http://dx.doi.org/10.1007/978-1-4612-2962-9
Descript	XVIII, 389 p. online resource

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

Interpretation of Seismic Signals -- Nonparametric deconvolution of seismic depth phases -- State space approach to signal extraction problems in seismology -- Improved signal transmission through randomization -- Online analysis of seismic signals -- Temperature Data -- Nonstationary time series analysis of monthly global temperature anomalies -- A test for detecting changes in mean -- Spatio-temporal modelling of temperature time series: a comparative study -- Modeling North Pacific climate time series -- Assortment of Important Time Series Problems and Applications -- Skew-elliptical time series with application to flooding risk -- Hidden periodicities analysis and its application in geophysics -- The innovation approach to the identification of nonlinear causal models in time series analysis -- Non-Gaussian time series models -- Modeling continuous time series driven by fractional Gaussian noise -- List of Workshop participants