Time Series Analysis and Its Applications: With R Examples, anonym.to

Time Series Analysis and Its Applications: With R Examples

Date: 24 Dec 2006, 20:30

Pages: 575

Time Series Analysis and Its Applications presents a balanced and comprehensive treatment

of both time and frequency domain methods with accompanying theory. Numerous examples

using non-trivial data illustrate solutions to problems such as evaluating pain perception

experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. The

book is designed to be useful as a text for graduate level students in the physical,

biological and social sciences and as a graduate level text in statistics. Some parts may

also serve as an undergraduate introductory course. Theory and methodology are separated

to allow presentations on different levels. Material from the earlier 1988 Prentice-Hall

text Applied Statistical Time Series Analysis has been updated by adding modern

developments involving categorical time sries analysis and the spectral envelope,

multivariate spectral methods, long memory series, nonlinear models, longitudinal data

analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte

Carlo Markov chain integration methods. These add to a classical coverage of time series

regression, univariate and multivariate ARIMA models, spectral analysis and state-space

models. The book is complemented by ofering accessibility, via the World Wide Web, to the

data and an exploratory time series analysis program ASTSA for Windows that can be

downloaded as Freeware. Robert H. Shumway is Professor of Statistics at the University of

California, Davis. He is a Fellow of the American Statistical Association and a member of

the Inernational Statistical Institute. He won the 1986 American Statistical Association

Award for Outstanding Statistical Application and the 1992 Communicable Diseases Center

Statistics Award; both awards were for joint papers on time series applications. He is the

author of a previous 1988 Prentice-Hall text on applied time series analysis and is

currenlty a Departmental Editor for the Journal of Forecasting. David S. Stoffer is

Professor of Statistics at the University of Pittsburgh. He has made seminal contributions

to the analysis of categorical time series and won the 1989 American Statistical

Association Award for Outstanding Statistical Application in a joint paper analyzing

categorical time series arising in infant sleep-state cycling. He is currently an

Associate Editor of the Journal of Forecasting and has served as an Associate Editor for

the Journal fo the American Statistical Association.

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