Murad S. Taqqu

Murray Rosenblatt

David Brillinger

Peter Caines

John Geweke

Emanuel Parzen

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.

Part I

Time Series Analysis and Applications to Geophysical Systems

This text focuses on linear stochastic models, whose theoretical foundations are the most fully worked out and the most frequently applied area of systems and control theory. Presents a unified and mathematically rigorous exposition of the main results of the theory of linear discrete-time-parameter stochastic systems. Begins with a thorough examination of the fundamentals of stochastic processes and the construction of stochastic systems, and goes on to provide an integrated treatment of the theories of prediction, regulation, modeling and estimation of system dynamics (system identification), and control. Text concludes with a presentation of stochastic adaptive control theory. Coverage of all topics incorporates the most recent research in the field.

Peter Caines

Books

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