Title	New Directions in Time Series Analysis [electronic resource] : Part II / edited by David Brillinger, Peter Caines, John Geweke, Emanuel Parzen, Murray Rosenblatt, Murad S. Taqqu
Imprint	New York, NY : Springer New York, 1993
Connect to	http://dx.doi.org/10.1007/978-1-4613-9296-5
Descript	XVIII, 382 p. online resource

This IMA Volume in Mathematics and its Applications NEW DIRECTIONS IN TIME SERIES ANALYSIS, PART II is based on the proceedings of the IMA summer program "New Directions in Time Series Analysis. " We are grateful to David Brillinger, Peter Caines, John Geweke, Emanuel Parzen, Murray Rosenblatt, and Murad Taqqu for organizing the program and we hope that the remarkable excitement and enthusiasm of the participants in this interdisciplinary effort are communicated to the reader. A vner Friedman Willard Miller, Jr. PREFACE Time Series Analysis is truly an interdisciplinary field because development of its theory and methods requires interaction between the diverse disciplines in which it is applied. To harness its great potential, strong interaction must be encouraged among the diverse community of statisticians and other scientists whose research involves the analysis of time series data. This was the goal of the IMA Workshop on "New Directions in Time Series Analysis. " The workshop was held July 2-July 27, 1990 and was organized by a committee consisting of Emanuel Parzen (chair), David Brillinger, Murray Rosenblatt, Murad S. Taqqu, John Geweke, and Peter Caines. Constant guidance and encouragement was provided by Avner Friedman, Director of the IMA, and his very helpful and efficient staff. The workshops were organized by weeks. It may be of interest to record the themes that were announced in the IMA newsletter describing the workshop: l

Recent developments in location estimation and regression for long-memory processes -- Phase-transition in statistical physical models with discrete and continuous symmetries -- Identification of linear systems from noisy data -- Unit roots in U.S. macroeconomic time series: A survey of classical and Bayesian perspectives -- A nonparametric approach to nonlinear time series analysis: Estimation and simulation -- Asymptotics of predictive stochastic complexity -- Smoothness priors -- An extension of quadrature-based methods for solving Euler conditions -- Long memory shot noises and limit theorems with application to Burgersโ equation -- On approximate modeling of linear Gaussian processes -- On the identification and prediction of nonlinear models -- Identification of stochastic time-varying parameters -- Convergence of Astrรถm-Wittenmarkโs self-tuning regulator and related topics -- On the closure of several sets of ARMA and linear state space models with a given structure -- Weak convergence to self-affine processes in dynamical systems -- Recursive estimation in ARMAX models -- On adaptive stabilization and ergodic behaviour of systems with Jump-Markov parameters via nonlinear filtering -- The convergence of output error recursions in infinite order moving average noise -- Linear models with long-range dependence and with finite or infinite variance -- Posterior analysis of possibly integrated time series with an application to real GNP -- On network structure function computations -- Asymptotic properties of estimates in incorrect ARMA models for long-memory time series

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