Time series analysis includes techniques for drawing conclusions from data recorded over a period of time. This book provides a modern introduction to time series analysis that will be useful as a reference to students in statistics, engineering, medicine, and economics. Robert H. Shumway is Professor of Statistics at the University of California, Davis, and David Stoffer is Professor of Statistics at the University of Pittsburgh.

Klappentext

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. Although designed as a text for graduate level students in statistics and the physical, biological and social sciences, some parts of the book will also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels, and the material has been updated by adding modern developments involving categorical time series 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. The book is supplemented by data and an exploratory time series analysis program ASTSA for Windows that can be downloaded from the Web as freeware.

Inhalt

1: Characteristics of Time Series.- 2: Time Series Regression and ARIMA Models.- 3: Spectral Analysis and Filtering.- 4: State-Space and Multivariate ARMAX Models.- 5: Statistical Methods in the Frequency Domain.- References.